This week’s  discussion paper for the PhD Seminar Series in Statistics is

The reading guide

In order to have more fun in the reading, we are aiming to complete  the two tasks before Sunday, Dec 22 .

  • Post at least one question here you found during the reading.
  • Give comments to at least one question that other people asked.

Happy reading!

By Feng Li

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. Feng obtained his Ph.D. degree in Statistics from Stockholm University, Sweden in 2013. His research interests include Bayesian computation, econometrics and forecasting, and distributed learning. His recent research output appeared in statistics and forecasting journals such as the International Journal of Forecasting and Statistical Analysis and Data Mining, AI journals such as Expert Systems with Applications, and medical journals such as BMJ Open.


  1. And I have one more:
    If you got to vote, would you prefer the name Bootstrap method or the name Shotgun method? (p.25)

  2. Here’s my first question:
    Would you say that there is any contradiction between “We have applied the bootstrap in a nonparametric way…”(p.25) and the discussion about the need to centralize the residuals so that \( \sum_{i=1}^n \epsilon_i=0 \) (p.17-19)?

  3. Notes on using LaTeX

    You can use standard LaTeX commands to display mathematical formulas.

    If you type

    Given that \( \boldsymbol{Y} = \boldsymbol{X}\beta + \epsilon \) 
    where \(\epsilon \sim N(\mu,\sigma^2)\)

    will give you

    Given that \( \boldsymbol{Y} = X\beta + \epsilon \) where \(\epsilon \sim N(\mu,\sigma^2)\).

    Also if you type

    \left( \sum_{k=1}^n a_k b_k \right)^{\!\!2} \leq
     \left( \sum_{k=1}^n a_k^2 \right) \left( \sum_{k=1}^n b_k^2 \right)

    will give you

    \[\left( \sum_{k=1}^n a_k b_k \right)^{\!\!2} \leq
    \left( \sum_{k=1}^n a_k^2 \right) \left( \sum_{k=1}^n b_k^2 \right)

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