Dr. Feng Li is an Associate Professor of Statistics in the School of Statistics and Mathematics at Central University of Finance and Economics in Beijing, China. He earned his Ph.D. degree in Statistics from Stockholm University, Sweden in 2013. Feng Li also served as the Associate Dean for the School of Statistics and Mathematics at Central University of Finance and Economics since 2016.
Education Background🎓
- Ph.D., 2013, Statistics, Stockholm University, Sweden.
- Thesis: Bayesian Modeling of Conditional Densities (The 2014 Cramér Prize, for the best Ph.D. thesis in Statistics and Mathematical Statistics, awarded by the Swedish Statistical Society)
- Supervisor: Prof. Mattias Villani
- Thesis opponent: Prof. Sylvia Frühwirth-Schnatter
- M.S., 2008, Statistics, Dalarna University, Sweden.
- B.S., 2007, Statistics, Renmin University of China (Outstanding graduate student, honored by Beijing Municipal Education Commission)
| Email: feng.li@cufe.edu.cn | Tel.: +86-(0)10-6177-6189 |
| Curriculum Vitae | 中文简历 |
Research Highlights👨🔬
Dr. Feng Li’s research interests include Bayesian Statistics, Econometrics and Forecasting, Distributed Learning. He develops highly scalable algorithms and software for solving real business problems. His recent research output appeared in top-tier journals like the Journal of Computational and Graphical Statistics, International Journal of Forecasting, Journal of Business and Economic Statistics, European Journal of Operational Research and Journal of the Operational Research Society. Feng Li has presented at the world meeting of the International Society for Bayesian Analysis (ISBA), and International Symposium on Forecasting.
Grants and Projects⚙️
- Complex Time Series Forecasting for E-commerce, Funded by Alibaba Innovative Research Program, (2021- ). Principal investigator.
- Development of the Methodologies of Objective Performance Criteria Based Single-Armed Trials for The Clinical Evaluation of Traditional Chinese Medicine. Funded by National Natural Science Foundation of China, (2020- ). Major Investigator.
- Efficient Bayesian Flexible Density Methods with High Dimensional Financial Data funded by National Natural Science Foundation of China, (2016-2019). Principal investigator.
- Bayesian Multivariate Density Estimation Methods for Complex Data. Funded by Ministry of Education, China (2014-2016). Principal investigator
Working Papers Under Review⏳
- Forecast combinations: an over 50-year review (with Xiaoqian Wang, Rob J Hyndman, and Yanfei Kang)
- [ Working Paper | Code ]
- Optimal reconciliation with immutable forecasts (with Bohan Zhang, Yanfei Kang, and Anastasios Panagiotelis)
- [ Working Paper | Code ]
- Feature-based intermittent demand forecast combinations: bias, accuracy and inventory implications. (with Li Li, Yanfei Kang and Fotios Petropoulos)
- [ Working Paper | Data ]
Selected Publications🗞️
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Li Li, Yanfei Kang and Feng Li (2022), “Bayesian forecast combination using time-varying features”, International Journal of Forecasting. (In Press)Abstract: In this work, we propose a novel framework for density forecast
combination by constructing time-varying weights based on time series
features, which is called Feature-based Bayesian Forecasting Model
Averaging (FEBAMA). Our framework estimates weights in the forecast
combination via Bayesian log predictive scores, in which the optimal
forecasting combination is determined by time series features from
historical information. In particular, we use an automatic Bayesian
variable selection method to add weight to the importance of different
features. To this end, our approach has better interpretability compared
to other black-box forecasting combination schemes. We apply our
framework to stock market data and M3 competition data. Based on our
structure, a simple maximum-a-posteriori scheme outperforms benchmark
methods, and Bayesian variable selection can further enhance the
accuracy for both point and density forecasts.BibTeX:@article{li2022bayesian_ijf, author = {Li, Li and Kang, Yanfei and Li, Feng}, title = {Bayesian forecast combination using time-varying features}, journal = {International Journal of Forecasting}, year = {2022}, number = {In Press}, url = {https://arxiv.org/abs/2108.02082} } -
Zhiru Wang, Yu Pang, Mingxin Gan, Martin Skitmore and Feng Li (2022), “Escalator accident mechanism analysis and injury prediction approaches in heavy capacity metro rail transit stations”, Safety Science. Vol. 154, pp. 105850.Abstract: The semi-open character with high passenger flow in Metro Rail Transport
Stations (MRTS) makes safety management of human-electromechanical
interaction escalator systems more complex. Safety management should not
consider only single failures, but also the complex interactions in the
system. This study applies task driven behavior theory and system theory
to reveal a generic framework of the MRTS escalator accident mechanism
and uses Lasso-Logistic Regression (LLR) for escalator injury
prediction. Escalator accidents in the Beijing MRTS are used as a case
study to estimate the applicability of the methodologies. The main
results affirm that the application of System-Theoretical Process
Analysis (STPA) and Task Driven Accident Process Analysis (TDAPA) to the
generic escalator accident mechanism reveals non-failure state task
driven passenger behaviors and constraints on safety that are not
addressed in previous studies. The results also confirm that LLR is able
to predict escalator accidents where there is a relatively large number
of variables with limited observations. Additionally, increasing the
amount of data improves the prediction accuracy for all three types of
injuries in the case study, suggesting the LLR model has good
extrapolation ability. The results can be applied in MRTS as instruments
for both escalator accident investigation and accident prevention.BibTeX:@article{wang2022escalator, author = {Zhiru Wang and Yu Pang and Mingxin Gan and Martin Skitmore and Feng Li}, title = {Escalator accident mechanism analysis and injury prediction approaches in heavy capacity metro rail transit stations}, journal = {Safety Science}, year = {2022}, volume = {154}, pages = {105850}, doi = {10.1016/j.ssci.2022.105850} } -
Xiaoqian Wang, Yanfei Kang, Fotios Petropoulos and Feng Li (2022), “The uncertainty estimation of feature-based forecast combinations”, Journal of the Operational Research Society. Vol. 73(5), pp. 979-993.Abstract: Forecasting is an indispensable element of operational research (OR) and
an important aid to planning. The accurate estimation of the forecast
uncertainty facilitates several operations management activities,
predominantly in supporting decisions in inventory and supply chain
management and effectively setting safety stocks. In this paper, we
introduce a feature-based framework, which links the relationship
between time series features and the interval forecasting performance
into providing reliable interval forecasts. We propose an optimal
threshold ratio searching algorithm and a new weight determination
mechanism for selecting an appropriate subset of models and assigning
combination weights for each time series tailored to the observed
features. We evaluate our approach using a large set of time series from
the M4 competition. Our experiments show that our approach significantly
outperforms a wide range of benchmark models, both in terms of point
forecasts as well as prediction intervals.BibTeX:@article{wang2022uncertainty_jors, author = {Wang, Xiaoqian and Kang, Yanfei and Petropoulos, Fotios and Li, Feng}, title = {The uncertainty estimation of feature-based forecast combinations}, journal = {Journal of the Operational Research Society}, year = {2022}, volume = {73}, number = {5}, pages = {979--993}, url = {https://arxiv.org/abs/1908.02891}, doi = {10.1080/01605682.2021.1880297} } -
Xiaoqian Wang, Yanfei Kang, Rob J. Hyndman and Feng Li (2022), “Distributed ARIMA models for ultra-long time series”, International Journal of Forecasting. (In Press)Abstract: Providing forecasts for ultra-long time series plays a vital role in
various activities, such as investment decisions, industrial production
arrangements, and farm management. This paper develops a novel
distributed forecasting framework to tackle challenges associated with
forecasting ultra-long time series by using the industry-standard
MapReduce framework. The proposed model combination approach facilitates
distributed time series forecasting by combining the local estimators of
time series models delivered from worker nodes and minimizing a global
loss function. In this way, instead of unrealistically assuming the data
generating process (DGP) of an ultra-long time series stays invariant,
we make assumptions only on the DGP of subseries spanning shorter time
periods. We investigate the performance of the proposed approach with
AutoRegressive Integrated Moving Average (ARIMA) models using the real
data application as well as numerical simulations. Compared to directly
fitting the whole data with ARIMA models, our approach results in
improved forecasting accuracy and computational efficiency both in point
forecasts and prediction intervals, especially for longer forecast
horizons. Moreover, we explore some potential factors that may affect
the forecasting performance of our approach.BibTeX:@article{wang2022distributed_ijf, author = {Wang, Xiaoqian and Kang, Yanfei and Hyndman, Rob J and Li, Feng}, title = {Distributed ARIMA models for ultra-long time series}, journal = {International Journal of Forecasting}, year = {2022}, number = {In Press}, url = {https://arxiv.org/abs/2007.09577}, doi = {10.1016/j.ijforecast.2022.05.001} } -
Matthias Anderer and Feng Li (2022), “Hierarchical forecasting with a top-down alignment of independent level forecasts”, International Journal of Forecasting. (In Press)Abstract: Hierarchical forecasting with intermittent time series is a challenge in
both research and empirical studies. Extensive research focuses on
improving the accuracy of each hierarchy, especially the intermittent
time series at bottom levels. Then, hierarchical reconciliation can be
used to improve the overall performance further. In this paper, we
present a hierarchical-forecasting-with-alignment approach that treats
the bottom-level forecasts as mutable to ensure higher forecasting
accuracy on the upper levels of the hierarchy. We employ a pure deep
learning forecasting approach, N-BEATS, for continuous time series at
the top levels, and a widely used tree-based algorithm, LightGBM, for
intermittent time series at the bottom level. The
hierarchical-forecasting-with-alignment approach is a simple yet
effective variant of the bottom-up method, accounting for biases that
are difficult to observe at the bottom level. It allows suboptimal
forecasts at the lower level to retain a higher overall performance. The
approach in this empirical study was developed by the first author
during the M5 Accuracy competition, ranking second place. The method is
also business orientated and can be used to facilitate strategic
business planning.BibTeX:@article{anderer2022forecasting_ijf, author = {Matthias Anderer and Feng Li}, title = {Hierarchical forecasting with a top-down alignment of independent level forecasts}, journal = {International Journal of Forecasting}, year = {2022}, number = {In Press}, url = {https://arxiv.org/abs/2103.08250}, doi = {10.1016/j.ijforecast.2021.12.015} } -
Fotios Petropoulos, Daniele Apiletti, Vassilios Assimakopoulos, Mohamed Zied Babai, Devon K. Barrow, Souhaib Ben Taieb, Christoph Bergmeir, Ricardo J. Bessa, Jakub Bijak, John E. Boylan, Jethro Browell, Claudio Carnevale, Jennifer L. Castle, Pasquale Cirillo, Michael P. Clements, Clara Cordeiro, Fernando Luiz Cyrino Oliveira, Shari De Baets, Alexander Dokumentov, Joanne Ellison, Piotr Fiszeder, Philip Hans Franses, David T. Frazier, Michael Gilliland, M. Sinan Gönül, Paul Goodwin, Luigi Grossi, Yael Grushka-Cockayne, Mariangela Guidolin, Massimo Guidolin, Ulrich Gunter, Xiaojia Guo, Renato Guseo, Nigel Harvey, David F. Hendry, Ross Hollyman, Tim Januschowski, Jooyoung Jeon, Victor Richmond R. Jose, Yanfei Kang, Anne B. Koehler, Stephan Kolassa, Nikolaos Kourentzes, Sonia Leva, Feng Li, Konstantia Litsiou, Spyros Makridakis, Gael M. Martin, Andrew B. Martinez, Sheik Meeran, Theodore Modis, Konstantinos Nikolopoulos, Dilek Önkal, Alessia Paccagnini, Anastasios Panagiotelis, Ioannis Panapakidis, Jose M. Pavía, Manuela Pedio, Diego J. Pedregal, Pierre Pinson, Patrícia Ramos, David E. Rapach, J. James Reade, Bahman Rostami-Tabar, Michał Rubaszek, Georgios Sermpinis, Han Lin Shang, Evangelos Spiliotis, Aris A. Syntetos, Priyanga Dilini Talagala, Thiyanga S. Talagala, Len Tashman, Dimitrios Thomakos, Thordis Thorarinsdottir, Ezio Todini, Juan Ramón Trapero Arenas, Xiaoqian Wang, Robert L. Winkler, Alisa Yusupova and Florian Ziel (2022), “Forecasting: theory and practice”, International Journal of Forecasting. Vol. 38(3), pp. 705-871.Abstract: Forecasting has always been at the forefront of decision making and
planning. The uncertainty that surrounds the future is both exciting and
challenging, with individuals and organisations seeking to minimise
risks and maximise utilities. The large number of forecasting
applications calls for a diverse set of forecasting methods to tackle
real-life challenges. This article provides a non-systematic review of
the theory and the practice of forecasting. We provide an overview of a
wide range of theoretical, state-of-the-art models, methods, principles,
and approaches to prepare, produce, organise, and evaluate forecasts. We
then demonstrate how such theoretical concepts are applied in a variety
of real-life contexts. We do not claim that this review is an exhaustive
list of methods and applications. However, we wish that our encyclopedic
presentation will offer a point of reference for the rich work that has
been undertaken over the last decades, with some key insights for the
future of forecasting theory and practice. Given its encyclopedic
nature, the intended mode of reading is non-linear. We offer
cross-references to allow the readers to navigate through the various
topics. We complement the theoretical concepts and applications covered
by large lists of free or open-source software implementations and
publicly-available databases.BibTeX:@article{petropoulos2021forecasting_ijf, author = {Fotios Petropoulos and Daniele Apiletti and Vassilios Assimakopoulos and Mohamed Zied Babai and Devon K. Barrow and Souhaib Ben Taieb and Christoph Bergmeir and Ricardo J. Bessa and Jakub Bijak and John E. Boylan and Jethro Browell and Claudio Carnevale and Jennifer L. Castle and Pasquale Cirillo and Michael P. Clements and Clara Cordeiro and Fernando Luiz Cyrino Oliveira and Shari De Baets and Alexander Dokumentov and Joanne Ellison and Piotr Fiszeder and Philip Hans Franses and David T. Frazier and Michael Gilliland and M. Sinan Gönül and Paul Goodwin and Luigi Grossi and Yael Grushka-Cockayne and Mariangela Guidolin and Massimo Guidolin and Ulrich Gunter and Xiaojia Guo and Renato Guseo and Nigel Harvey and David F. Hendry and Ross Hollyman and Tim Januschowski and Jooyoung Jeon and Victor Richmond R. Jose and Yanfei Kang and Anne B. Koehler and Stephan Kolassa and Nikolaos Kourentzes and Sonia Leva and Feng Li and Konstantia Litsiou and Spyros Makridakis and Gael M. Martin and Andrew B. Martinez and Sheik Meeran and Theodore Modis and Konstantinos Nikolopoulos and Dilek Önkal and Alessia Paccagnini and Anastasios Panagiotelis and Ioannis Panapakidis and Jose M. Pavía and Manuela Pedio and Diego J. Pedregal and Pierre Pinson and Patrícia Ramos and David E. Rapach and J. James Reade and Bahman Rostami-Tabar and Michał Rubaszek and Georgios Sermpinis and Han Lin Shang and Evangelos Spiliotis and Aris A. Syntetos and Priyanga Dilini Talagala and Thiyanga S. Talagala and Len Tashman and Dimitrios Thomakos and Thordis Thorarinsdottir and Ezio Todini and Juan Ramón Trapero Arenas and Xiaoqian Wang and Robert L. Winkler and Alisa Yusupova and Florian Ziel}, title = {Forecasting: theory and practice}, journal = {International Journal of Forecasting}, year = {2022}, volume = {38}, number = {3}, pages = {705--871}, url = {https://arxiv.org/abs/2012.03854}, doi = {10.1016/j.ijforecast.2021.11.001} } -
Thiyanga S. Talagala, Feng Li and Yanfei Kang (2022), “FFORMPP: Feature-based forecast model performance prediction”, International Journal of Forecasting. Vol. 38(3), pp. 920-943.Abstract: This paper introduces a novel meta-learning algorithm for time series
forecast model performance prediction. We model the forecast error as a
function of time series features calculated from historical time series
with an efficient Bayesian multivariate surface regression approach. The
minimum predicted forecast error is then used to identify an individual
model or a combination of models to produce the final forecasts. It is
well known that the performance of most meta-learning models depends on
the representativeness of the reference dataset used for training. In
such circumstances, we augment the reference dataset with a
feature-based time series simulation approach, namely GRATIS, to
generate a rich and representative time series collection. The proposed
framework is tested using the M4 competition data and is compared
against commonly used forecasting approaches. Our approach provides
comparable performance to other model selection and combination
approaches but at a lower computational cost and a higher degree of
interpretability, which is important for supporting decisions. We also
provide useful insights regarding which forecasting models are expected
to work better for particular types of time series, the intrinsic
mechanisms of the meta-learners, and how the forecasting performance is
affected by various factors.BibTeX:@article{talagala2022fformpp_ijf, author = {Talagala, Thiyanga S and Li, Feng and Kang, Yanfei}, title = {FFORMPP: Feature-based forecast model performance prediction}, journal = {International Journal of Forecasting}, year = {2022}, volume = {38}, number = {3}, pages = {920--943}, url = {https://arxiv.org/abs/1908.11500}, doi = {10.1016/j.ijforecast.2021.07.002} } -
Yanfei Kang, Wei Cao, Fotios Petropoulos and Feng Li (2022), “Forecast with Forecasts: Diversity Matters”, European Journal of Operational Research. Vol. 31(1), pp. 180-190.Abstract: Forecast combinations have been widely applied in the last few decades
to improve forecasting. Estimating optimal weights that can outperform
simple averages is not always an easy task. In recent years, the idea of
using time series features for forecast combinations has
flourished. Although this idea has been proved to be beneficial in
several forecasting competitions, it may not be practical in many
situations. For example, the task of selecting appropriate features to
build forecasting models is often challenging. Even if there was an
acceptable way to define the features, existing features are estimated
based on the historical patterns, which are likely to change in the
future. Other times, the estimation of the features is infeasible due to
limited historical data. In this work, we suggest a change of focus from
the historical data to the produced forecasts to extract features. We
use out-of-sample forecasts to obtain weights for forecast combinations
by amplifying the diversity of the pool of methods being combined. A
rich set of time series is used to evaluate the performance of the
proposed method. Experimental results show that our diversity-based
forecast combination framework not only simplifies the modeling process
but also achieves superior forecasting performance in terms of both
point forecasts and prediction intervals. The value of our proposition
lies on its simplicity, transparency, and computational efficiency,
elements that are important from both an optimization and a decision
analysis perspective.BibTeX:@article{kang2022forecast_ejor, author = {Kang, Yanfei and Cao, Wei and Petropoulos, Fotios and Li, Feng}, title = {Forecast with Forecasts: Diversity Matters}, journal = {European Journal of Operational Research}, year = {2022}, volume = {31}, number = {1}, pages = {180--190}, url = {https://arxiv.org/abs/2012.01643}, doi = {10.1016/j.ejor.2021.10.024} } -
Xuening Zhu, Feng Li and Hansheng Wang (2021), “Least-Square Approximation for a Distributed System”, Journal of Computational and Graphical Statistics. Vol. 30(4), pp. 1004-1018.Abstract: In this work, we develop a distributed least-square approximation (DLSA)
method that is able to solve a large family of regression problems
(e.g., linear regression, logistic regression, and Cox’s model) on a
distributed system. By approximating the local objective function using
a local quadratic form, we are able to obtain a combined estimator by
taking a weighted average of local estimators. The resulting estimator
is proved to be statistically as efficient as the global
estimator. Moreover, it requires only one round of communication. We
further conduct a shrinkage estimation based on the DLSA estimation
using an adaptive Lasso approach. The solution can be easily obtained by
using the LARS algorithm on the master node. It is theoretically shown
that the resulting estimator possesses the oracle property and is
selection consistent by using a newly designed distributed Bayesian
information criterion. The finite sample performance and computational
efficiency are further illustrated by an extensive numerical study and
an airline dataset. The airline dataset is 52 GB in size. The entire
methodology has been implemented in Python for a de-facto standard Spark
system. The proposed DLSA algorithm on the Spark system takes 26 min to
obtain a logistic regression estimator, which is more efficient and
memory friendly than conventional methods. Supplementary materials for
this article are available online.BibTeX:@article{zhu2021least_jcgs, author = {Zhu, Xuening and Li, Feng and Wang, Hansheng}, title = {Least-Square Approximation for a Distributed System}, journal = {Journal of Computational and Graphical Statistics}, year = {2021}, volume = {30}, number = {4}, pages = {1004--1018}, url = {https://arxiv.org/abs/1908.04904}, doi = {10.1080/10618600.2021.1923517} } -
Rui Pan, Tunan Ren, Baishan Guo, Feng Li, Guodong Li and Hansheng Wang (2021), “A Note on Distributed Quantile Regression by Pilot Sampling and One-Step Updating”, Journal of Business and Economic Statistics. (In Press)Abstract: Quantile regression is a method of fundamental importance. How to
efficiently conduct quantile regression for a large dataset on a
distributed system is of great importance. We show that the popularly
used one-shot estimation is statistically inefficient if data are not
randomly distributed across different workers. To fix the problem, a
novel one-step estimation method is developed with the following nice
properties. First, the algorithm is communication efficient. That is the
communication cost demanded is practically acceptable. Second, the
resulting estimator is statistically efficient. That is its asymptotic
covariance is the same as that of the global estimator. Third, the
estimator is robust against data distribution. That is its consistency
is guaranteed even if data are not randomly distributed across different
workers. Numerical experiments are provided to corroborate our
findings. A real example is also presented for illustration.BibTeX:@article{pan2021note_jbes, author = {Rui Pan and Tunan Ren and Baishan Guo and Feng Li and Guodong Li and Hansheng Wang}, title = {A Note on Distributed Quantile Regression by Pilot Sampling and One-Step Updating}, journal = {Journal of Business and Economic Statistics}, year = {2021}, number = {In Press}, doi = {10.1080/07350015.2021.1961789} } -
Yanfei Kang, Evangelos Spiliotis, Fotios Petropoulos, Nikolaos Athiniotis, Feng Li and Vassilios Assimakopoulos (2021), “Déjà vu: A data-centric forecasting approach through time series cross-similarity”, Journal of Business Research. Vol. 132(2021), pp. 719-731.Abstract: Accurate forecasts are vital for supporting the decisions of modern
companies. Forecasters typically select the most appropriate statistical
model for each time series. However, statistical models usually presume
some data generation process while making strong assumptions about the
errors. In this paper, we present a novel data-centric approach —
‘forecasting with cross-similarity’, which tackles model uncertainty in
a model-free manner. Existing similarity-based methods focus on
identifying similar patterns within the series, i.e.,
‘self-similarity’. In contrast, we propose searching for similar
patterns from a reference set, i.e., ‘cross-similarity’. Instead of
extrapolating, the future paths of the similar series are aggregated to
obtain the forecasts of the target series. Building on the
cross-learning concept, our approach allows the application of
similarity-based forecasting on series with limited lengths. We evaluate
the approach using a rich collection of real data and show that it
yields competitive accuracy in both points forecasts and prediction
intervals.BibTeX:@article{kang2021deja_jbr, author = {Kang, Yanfei and Spiliotis, Evangelos and Petropoulos, Fotios and Athiniotis, Nikolaos and Li, Feng and Assimakopoulos, Vassilios}, title = {Déjà vu: A data-centric forecasting approach through time series cross-similarity}, journal = {Journal of Business Research}, year = {2021}, volume = {132}, number = {2021}, pages = {719--731}, url = {https://arxiv.org/abs/1909.00221}, doi = {10.1016/j.jbusres.2020.10.051} } -
Megan G. Janeway, Xiang Zhao, Max Rosenthaler, Yi Zuo, Kumar Balasubramaniyan, Michael Poulson, Miriam Neufeld, Jeffrey J. Siracuse, Courtney E. Takahashi, Lisa Allee, Tracey Dechert, Peter A. Burke, Feng Li and Bindu Kalesan (2021), “Clinical diagnostic phenotypes in hospitalizations due to self-inflicted firearm injury”, Journal of Affective Disorders. Vol. 278, pp. 172-180.Abstract: Hospitalized self-inflicted firearm injuries have not been extensively
studied, particularly regarding clinical diagnoses at the index
admission. The objective of this study was to discover the diagnostic
phenotypes (DPs) or clusters of hospitalized self-inflicted firearm
injuries. Using Nationwide Inpatient Sample data in the US from 1993 to
2014, we used International Classification of Diseases, Ninth Revision
codes to identify self-inflicted firearm injuries among those ≥18 years
of age. The 25 most frequent diagnostic codes were used to compute a
dissimilarity matrix and the optimal number of clusters. We used
hierarchical clustering to identify the main DPs. The overall cohort
included 14072 hospitalizations, with self-inflicted firearm injuries
occurring mainly in those between 16 to 45 years of age, black, with
co-occurring tobacco and alcohol use, and mental illness. Out of the
three identified DPs, DP1 was the largest (n=10,110), and included most
common diagnoses similar to overall cohort, including major depressive
disorders (27.7%), hypertension (16.8%), acute post hemorrhagic anemia
(16.7%), tobacco (15.7%) and alcohol use (12.6%). DP2 (n=3,725) was
not characterized by any of the top 25 ICD-9 diagnoses codes, and
included children and peripartum women. DP3, the smallest phenotype
(n=237), had high prevalence of depression similar to DP1, and defined
by fewer fatal injuries of chest and abdomen. There were three distinct
diagnostic phenotypes in hospitalizations due to self-inflicted firearm
injuries. Further research is needed to determine how DPs can be used to
tailor clinical care and prevention efforts.BibTeX:@article{janeway2021clinical_jad, author = {Megan G Janeway and Xiang Zhao and Max Rosenthaler and Yi Zuo and Kumar Balasubramaniyan and Michael Poulson and Miriam Neufeld and Jeffrey J. Siracuse and Courtney E. Takahashi and Lisa Allee and Tracey Dechert and Peter A Burke and Feng Li and Bindu Kalesan}, title = {Clinical diagnostic phenotypes in hospitalizations due to self-inflicted firearm injury}, journal = {Journal of Affective Disorders}, year = {2021}, volume = {278}, pages = {172--180}, doi = {10.1016/j.jad.2020.09.067} } -
BibTeX:
@book{li2020fppcn, author = {康雁飞 and 李丰}, title = {预测：方法与实践}, publisher = {在线出版}, year = {2020}, url = {https://otexts.com/fppcn/} } -
BibTeX:
@book{kang2020statscompcn, author = {康雁飞 and 李丰}, title = {统计计算}, publisher = {在线出版}, year = {2020}, url = {https://feng.li/files/statscompbook/} } -
Bindu Kalesan, Siran Zhao, Michael Poulson, Miriam Neufeld, Tracey Dechert, Jeffrey J. Siracuse, Yi Zuo and Feng Li (2020), “Intersections of firearm suicide, drug-related mortality, and economic dependency in rural America”, Journal of Surgical Research. Vol. 256, pp. 96-102.Abstract: Rural counties in the United States have higher firearm suicide rates
and opioid overdoses than urban counties. We sought to determine whether
rural counties can be grouped based on these “diseases of despair.”
Age-adjusted firearm suicide death rates per 100,000; drug-related death
rates per 100,000; homicide rate per 100,000, opioid prescribing rate,
%black, %Native American, and %veteran population, median home price,
violent crime rates per 100,000, primary economic dependency
(nonspecialized, farming, mining, manufacturing, government, and
recreation), and economic variables (low education, low employment,
retirement destination, persistent poverty, and persistent child
poverty) were obtained for all rural counties and evaluated with
hierarchical clustering using complete linkage. We identified five
distinct rural county clusters. The firearm suicide rates in the
clusters were 5.9, 6.8, 6.4, 8.5, and 3.8 per 100,000, respectively. The
counties in cluster 1 were poor, mining dependent, with population loss,
cluster 2 were nonspecialized economies, with high opioid prescription
rates, cluster 3 were manufacturing and government economies with
moderate unemployment, cluster 4 were recreational economies with
substantial veterans and Native American populations, high median home
price, drug death rates, opioid prescribing, and violent crime, and
cluster 5 were farming economies, with high population loss, low median
home price, low rates of drug mortality, opioid prescribing, and violent
crime. Cluster 4 counties were spatially adjacent to urban
counties. More than 300 counties currently face a disproportionate
burden of diseases of despair. Interventions to reduce firearm suicides
should be community-based and include programs to reduce other diseases
of despair.BibTeX:@article{kalesan2020intersections_jsr, author = {Kalesan, Bindu and Zhao, Siran and Poulson, Michael and Neufeld, Miriam and Dechert, Tracey and Siracuse, Jeffrey J and Zuo, Yi and Li, Feng}, title = {Intersections of firearm suicide, drug-related mortality, and economic dependency in rural America}, journal = {Journal of Surgical Research}, year = {2020}, volume = {256}, pages = {96--102}, doi = {10.1016/j.jss.2020.06.011} } -
Xixi Li, Yanfei Kang and Feng Li (2020), “Forecasting with time series imaging”, Expert Systems with Applications. Vol. 160, pp. 113680.Abstract: Feature-based time series representations have attracted substantial
attention in a wide range of time series analysis methods. Recently, the
use of time series features for forecast model averaging has been an
emerging research focus in the forecasting community. Nonetheless, most
of the existing approaches depend on the manual choice of an appropriate
set of features. Exploiting machine learning methods to extract features
from time series automatically becomes crucial in state-of-the-art time
series analysis. In this paper, we introduce an automated approach to
extract time series features based on time series imaging. We first
transform time series into recurrence plots, from which local features
can be extracted using computer vision algorithms. The extracted
features are used for forecast model averaging. Our experiments show
that forecasting based on automatically extracted features, with less
human intervention and a more comprehensive view of the raw time series
data, yields highly comparable performances with the best methods in the
largest forecasting competition dataset (M4) and outperforms the top
methods in the Tourism forecasting competition dataset.BibTeX:@article{li2020forecasting_eswa, author = {Li, Xixi and Kang, Yanfei and Li, Feng}, title = {Forecasting with time series imaging}, journal = {Expert Systems with Applications}, year = {2020}, volume = {160}, pages = {113680}, url = {https://arxiv.org/abs/1904.08064}, doi = {10.1016/j.eswa.2020.113680} } -
Chengcheng Hao, Feng Li and Dietrich von Rosen (2020), “A Bilinear Reduced Rank Model”, In Contemporary Experimental Design, Multivariate Analysis and Data Mining. Springer Nature.Abstract: This article considers a bilinear model that includes two different
latent effects. The first effect has a direct influence on the response
variable, whereas the second latent effect is assumed to first influence
other latent variables, which in turn affect the response variable. In
this article, latent variables are modelled via rank restrictions on
unknown mean parameters and the models which are used are often referred
to as reduced rank regression models. This article presents a
likelihood-based approach that results in explicit estimators. In our
model, the latent variables act as covariates that we know exist, but
their direct influence is unknown and will therefore not be considered
in detail. One example is if we observe hundreds of weather variables,
but we cannot say which or how these variables affect plant growth.BibTeX:@inbook{hao2020bilinear_ced, author = {Hao, Chengcheng and Li, Feng and von Rosen, Dietrich}, editor = {Jianqing Fan and Jianxin Pan}, title = {A Bilinear Reduced Rank Model}, booktitle = {Contemporary Experimental Design, Multivariate Analysis and Data Mining}, publisher = {Springer Nature}, year = {2020}, doi = {10.1007/978-3-030-46161-4_21} } -
Yanfei Kang, Rob J. Hyndman and Feng Li (2020), “GRATIS: GeneRAting TIme Series with diverse and controllable characteristics”, Statistical Analysis and Data Mining. Vol. 13, pp. 354-376.Abstract: The explosion of time series data in recent years has brought a flourish
of new time series analysis methods, for forecasting, clustering,
classification and other tasks. The evaluation of these new methods
requires either collecting or simulating a diverse set of time series
benchmarking data to enable reliable comparisons against alternative
approaches. We propose GeneRAting TIme Series with diverse and
controllable characteristics, named GRATIS, with the use of mixture
autoregressive (MAR) models. We simulate sets of time series using MAR
models and investigate the diversity and coverage of the generated time
series in a time series feature space. By tuning the parameters of the
MAR models, GRATIS is also able to efficiently generate new time series
with controllable features. In general, as a costless surrogate to the
traditional data collection approach, GRATIS can be used as an
evaluation tool for tasks such as time series forecasting and
classification. We illustrate the usefulness of our time series
generation process through a time series forecasting application.BibTeX:@article{kang2020gratis_sam, author = {Kang, Yanfei and Hyndman, Rob J and Li, Feng}, title = {GRATIS: GeneRAting TIme Series with diverse and controllable characteristics}, journal = {Statistical Analysis and Data Mining}, year = {2020}, volume = {13}, pages = {354--376}, url = {https://arxiv.org/abs/1903.02787}, doi = {10.1002/sam.11461} } -
Hannah M. Bailey, Yi Zuo, Feng Li, Jae Min, Krishna Vaddiparti, Mattia Prosperi, Jeffrey Fagan, Sandro Galea and Bindu Kalesan (2019), “Changes in patterns of mortality rates and years of life lost due to firearms in the United States, 1999 to 2016: A joinpoint analysis”, PLoS One. Vol. 14(11)Abstract: Firearm-related death rates and years of potential life lost (YPLL) vary
widely between population subgroups and states. However, changes or
inflections in temporal trends within subgroups and states are not fully
documented. We assessed temporal patterns and inflections in the rates
of firearm deaths and %YPLL due to firearms for overall and by sex,
age, race/ethnicity, intent, and states in the United States between
1999 and 2016. We extracted age-adjusted firearm mortality and YPLL
rates per 100,000, and %YPLL from 1999 to 2016 by using the WONDER
(Wide-ranging Online Data for Epidemiologic Research) database. We used
Joinpoint Regression to assess temporal trends, the inflection points,
and annual percentage change (APC) from 1999 to 2016. National firearm
mortality rates were 10.3 and 11.8 per 100,000 in 1999 and 2016, with
two distinct segments; a plateau until 2014 followed by an increase of
APC = 7.2% (95% CI 3.1, 11.4). YPLL rates were from 304.7 and 338.2 in
1999 and 2016 with a steady APC increase in %YPLL of 0.65% (95% CI
0.43, 0.87) from 1999 to an inflection point in 2014, followed by a
larger APC in %YPLL of 5.1% (95% CI 0.1, 10.4). The upward trend in
firearm mortality and YPLL rates starting in 2014 was observed in
subgroups of male, non-Hispanic blacks, Hispanic whites and for firearm
assaults. The inflection points for firearm mortality and YPLL rates
also varied across states. Within the United States, firearm mortality
rates and YPLL remained constant between 1999 and 2014 and has been
increasing subsequently. There was, however, an increase in firearm
mortality rates in several subgroups and individual states earlier than
2014.BibTeX:@article{bailey2019changes_plosone, author = {Bailey, Hannah M and Zuo, Yi and Li, Feng and Min, Jae and Vaddiparti, Krishna and Prosperi, Mattia and Fagan, Jeffrey and Galea, Sandro and Kalesan, Bindu}, title = {Changes in patterns of mortality rates and years of life lost due to firearms in the United States, 1999 to 2016: A joinpoint analysis}, journal = {PLoS One}, year = {2019}, volume = {14}, number = {11}, doi = {10.1371/journal.pone.0225223} } -
Feng Li and Zhuojing He (2019), “Credit risk clustering in a business group: which matters more, systematic or idiosyncratic risk?”, Cogent Economics & Finance. , pp. 1632528.Abstract: Understanding how defaults correlate across firms is a persistent
concern in risk management. In this paper, we apply covariate-dependent
copula models to assess the dynamic nature of credit risk dependence,
which we define as “credit risk clustering”. We also study the driving
forces of the credit risk clustering in CEC business group in China. Our
empirical analysis shows that the credit risk clustering varies over
time and exhibits different patterns across firm pairs in a business
group. We also investigate the impacts of systematic and idiosyncratic
factors on credit risk clustering. We find that the impacts of the money
supply and the short-term interest rates are positive, whereas the
impacts of exchange rates are negative. The roles of the CPI on credit
risk clustering are ambiguous. Idiosyncratic factors are vital for
predicting credit risk clustering. From a policy perspective, our
results not only strengthen the results of previous research but also
provide a possible approach to model and predict the extreme co-movement
of credit risk in business groups with financial indicators.BibTeX:@article{li2019credit_cef, author = {Li, Feng and He, Zhuojing}, title = {Credit risk clustering in a business group: which matters more, systematic or idiosyncratic risk?}, journal = {Cogent Economics & Finance}, year = {2019}, pages = {1632528}, url = {http://dx.doi.org/10.2139/ssrn.3182925}, doi = {10.1080/23322039.2019.1632528} } -
Elizabeth C. Pino, Yi Zuo, Camila Maciel De Olivera, Shruthi Mahalingaiah, Olivia Keiser, Lynn L. Moore, Feng Li, Ramachandran S. Vasan, Barbara E. Corkey and Bindu Kalesan (2018), “Cohort profile: The MULTI sTUdy Diabetes rEsearch (MULTITUDE) consortium”, BMJ Open. Vol. 8(5), pp. e020640.Abstract: Globally, the age-standardised prevalence of type 2 diabetes mellitus
(T2DM) has nearly doubled from 1980 to 2014, rising from 4.7 to 8.5 with
an estimated 422 million adults living with the chronic disease. The
MULTI sTUdy Diabetes rEsearch (MULTITUDE) consortium was recently
established to harmonise data from 17 independent cohort studies and
clinical trials and to facilitate a better understanding of the
determinants, risk factors and outcomes associated with
T2DM. Participants Participants range in age from 3 to 88 years at
baseline, including both individuals with and without T2DM. MULTITUDE is
an individual-level pooled database of demographics, comorbidities,
relevant medications, clinical laboratory values, cardiac health
measures, and T2DM-associated events and outcomes across 45 US states
and the District of Columbia. Findings to date Among the 135 156 ongoing
participants included in the consortium, almost 25% (33 421) were
diagnosed with T2DM at baseline. The average age of the participants was
54.3%, while the average age of participants with diabetes was
64.2%. Men (55.3%) and women (44.6%) were almost equally represented
across the consortium. Non-whites accounted for 31.6 of the total
participants and 40% of those diagnosed with T2DM. Fewer individuals
with diabetes reported being regular smokers than their non-diabetic
counterparts (40.3% vs 47.4%). Over 85% of those with diabetes were
reported as either overweight or obese at baseline, compared with 60.7%
of those without T2DM. We observed differences in all-cause mortality,
overall and by T2DM status, between cohorts. Given the wide variation in
demographics and all-cause mortality in the cohorts, MULTITUDE
consortium will be a unique resource for conducting research to
determine: differences in the incidence and progression of T2DM;
sequence of events or biomarkers prior to T2DM diagnosis; disease
progression from T2DM to disease-related outcomes, complications and
premature mortality; and to assess race/ethnicity differences in the
above associations.BibTeX:@article{pino2018cohort_bmj, author = {Pino, Elizabeth C and Zuo, Yi and De Olivera, Camila Maciel and Mahalingaiah, Shruthi and Keiser, Olivia and Moore, Lynn L and Li, Feng and Vasan, Ramachandran S and Corkey, Barbara E and Kalesan, Bindu}, title = {Cohort profile: The MULTI sTUdy Diabetes rEsearch (MULTITUDE) consortium}, journal = {BMJ Open}, year = {2018}, volume = {8}, number = {5}, pages = {e020640}, doi = {10.1136/bmjopen-2017-020640} } -
Feng Li and Yanfei Kang (2018), “Improving forecasting performance using covariate-dependent copula models”, International Journal of Forecasting. Vol. 34(3), pp. 456-476.Abstract: Copulas provide an attractive approach to the construction of
multivariate distributions with flexible marginal distributions and
different forms of dependences. Of particular importance in many areas
is the possibility of forecasting the tail-dependences explicitly. Most
of the available approaches are only able to estimate tail-dependences
and correlations via nuisance parameters, and cannot be used for either
interpretation or forecasting. We propose a general Bayesian approach
for modeling and forecasting tail-dependences and correlations as
explicit functions of covariates, with the aim of improving the copula
forecasting performance. The proposed covariate-dependent copula model
also allows for Bayesian variable selection from among the covariates of
the marginal models, as well as the copula density. The copulas that we
study include the Joe-Clayton copula, the Clayton copula, the Gumbel
copula and the Student’s -copula. Posterior inference is carried out
using an efficient MCMC simulation method. Our approach is applied to
both simulated data and the S&P 100 and S&P 600 stock indices. The
forecasting performance of the proposed approach is compared with those
of other modeling strategies based on log predictive scores. A
value-at-risk evaluation is also performed for the model comparisons.BibTeX:@article{li2018improving_ijf, author = {Li, Feng and Kang, Yanfei}, title = {Improving forecasting performance using covariate-dependent copula models}, journal = {International Journal of Forecasting}, year = {2018}, volume = {34}, number = {3}, pages = {456--476}, url = {https://arxiv.org/abs/1401.0100}, doi = {10.1016/j.ijforecast.2018.01.007} } -
BibTeX:
@book{li2016distributedcn, author = {李丰}, title = {大数据分布式计算与案例}, publisher = {中国人民大学出版社}, year = {2016}, url = {https://feng.li/files/distcompbook/} } -
Feng Li (2013), “Bayesian Modeling of Conditional Densities”. Thesis at: Department of Statistics, Stockholm University.Abstract: This thesis develops models and associated Bayesian inference methods
for flexible univariate and multivariate conditional density
estimation. The models are flexible in the sense that they can capture
widely differing shapes of the data. The estimation methods are
specifically designed to achieve flexibility while still avoiding
overfitting. The models are flexible both for a given covariate value,
but also across covariate space. A key contribution of this thesis is
that it provides general approaches of density estimation with highly
efficient Markov chain Monte Carlo methods. The methods are illustrated
on several challenging non-linear and non-normal datasets. In the first
paper, a general model is proposed for flexibly estimating the density
of a continuous response variable conditional on a possibly
high-dimensional set of covariates. The model is a finite mixture of
asymmetric student-t densities with covariate-dependent mixture
weights. The four parameters of the components, the mean, degrees of
freedom, scale and skewness, are all modeled as functions of the
covariates. The second paper explores how well a smooth mixture of
symmetric components can capture skewed data. Simulations and
applications on real data show that including covariate-dependent
skewness in the components can lead to substantially improved
performance on skewed data, often using a much smaller number of
components. We also introduce smooth mixtures of gamma and log-normal
components to model positively-valued response variables. In the third
paper we propose a multivariate Gaussian surface regression model that
combines both additive splines and interactive splines, and a highly
efficient MCMC algorithm that updates all the multi-dimensional knot
locations jointly. We use shrinkage priors to avoid overfitting with
different estimated shrinkage factors for the additive and surface part
of the model, and also different shrinkage parameters for the different
response variables. In the last paper we present a general Bayesian
approach for directly modeling dependencies between variables as
function of explanatory variables in a flexible copula context. In
particular, the Joe-Clayton copula is extended to have
covariate-dependent tail dependence and correlations. Posterior
inference is carried out using a novel and efficient simulation
method. The appendix of the thesis documents the computational
implementation details.BibTeX:@phdthesis{li2013bayesian, author = {Li, Feng}, title = {Bayesian Modeling of Conditional Densities}, school = {Department of Statistics, Stockholm University}, year = {2013}, note = {ISBN: 978-91-7447-665-1}, url = {http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-89426} } -
Feng Li and Mattias Villani (2013), “Efficient Bayesian Multivariate Surface Regression”, Scandinavian Journal of Statistics. Vol. 40(4), pp. 706-723.Abstract: Methods for choosing a fixed set of knot locations in additive spline
models are fairly well established in the statistical literature. The
curse of dimensionality makes it nontrivial to extend these methods to
nonadditive surface models, especially when there are more than a couple
of covariates. We propose a multivariate Gaussian surface regression
model that combines both additive splines and interactive splines, and a
highly efficient Markov chain Monte Carlo algorithm that updates all the
knot locations jointly. We use shrinkage prior to avoid overfitting with
different estimated shrinkage factors for the additive and surface part
of the model, and also different shrinkage parameters for the different
response variables. Simulated data and an application to firm leverage
data show that the approach is computationally efficient, and that
allowing for freely estimated knot locations can offer a substantial
improvement in out-of-sample predictive performance.BibTeX:@article{li2013efficient_sjs, author = {Li, Feng and Villani, Mattias}, title = {Efficient Bayesian Multivariate Surface Regression}, journal = {Scandinavian Journal of Statistics}, year = {2013}, volume = {40}, number = {4}, pages = {706--723}, url = {https://arxiv.org/abs/1110.3689}, doi = {10.1111/sjos.12022} } -
Feng Li, Mattias Villani and Robert Kohn (2011), “Modeling Conditional Densities Using Finite Smooth Mixtures”, In Mixtures: estimation and applications. , pp. 123-144. John Wiley & Sons Inc, Chichester.Abstract: Smooth mixtures, i.e. mixture models with covariate-dependent mixing
weights, are very useful flexible models for conditional
densities. Previous work shows that using too simple mixture components
for modeling heteroscedastic and/or heavy tailed data can give a poor
fit, even with a large number of components. This paper explores how
well a smooth mixture of symmetric components can capture skewed
data. Simulations and applications on real data show that including
covariate-dependent skewness in the components can lead to substantially
improved performance on skewed data, often using a much smaller number
of components. Furthermore, variable selection is effective in removing
unnecessary covariates in the skewness, which means that there is little
loss in allowing for skewness in the components when the data are
actually symmetric. We also introduce smooth mixtures of gamma and
log-normal components to model positively-valued response variables.BibTeX:@inbook{li2011modeling_mixtures, author = {Li, Feng and Villani, Mattias and Kohn, Robert}, editor = {Mengersen, Kerrie and Robert, Christian and Titterington, Mike}, title = {Modeling Conditional Densities Using Finite Smooth Mixtures}, booktitle = {Mixtures: estimation and applications}, publisher = {John Wiley & Sons Inc, Chichester}, year = {2011}, pages = {123--144}, url = {http://dx.doi.org/10.2139/ssrn.1711194}, doi = {10.1002/9781119995678.ch6} } -
Feng Li, Mattias Villani and Robert Kohn (2010), “Flexible modeling of conditional distributions using smooth mixtures of asymmetric student t densities”, Journal of Statistical Planning and Inference. Vol. 140(12), pp. 3638-3654.Abstract: A general model is proposed for flexibly estimating the density of a
continuous response variable conditional on a possibly high-dimensional
set of covariates. The model is a finite mixture of asymmetric student t
densities with covariate-dependent mixture weights. The four parameters
of the components, the mean, degrees of freedom, scale and skewness, are
all modeled as functions of the covariates. Inference is Bayesian and
the computation is carried out using Markov chain Monte Carlo
simulation. To enable model parsimony, a variable selection prior is
used in each set of covariates and among the covariates in the mixing
weights. The model is used to analyze the distribution of daily stock
market returns, and shown to more accurately forecast the distribution
of returns than other widely used models for financial data.BibTeX:@article{li2010flexible_jspi, author = {Li, Feng and Villani, Mattias and Kohn, Robert}, title = {Flexible modeling of conditional distributions using smooth mixtures of asymmetric student t densities}, journal = {Journal of Statistical Planning and Inference}, year = {2010}, volume = {140}, number = {12}, pages = {3638--3654}, url = {http://dx.doi.org/10.2139/ssrn.1551195}, doi = {10.1016/j.jspi.2010.04.031} }