Reading Group
Take the bull (research) by the horns (skills)!
#6 Large Sample Techniques for Statistics (2020-21 Summer)
Part (I): Book reading
(1 June 2021) Chapters 1--2: The ε-δ Arguments + Modes of Convergence (By Tom) [video]
(3 June 2021) Chapters 3--4: Big O, Small o, and the Unspecified c + Asymptotic Expansions (By James) [video]
(8 June 2021) Chapters 5--6: Inequalities + Sums of Independent RandomVariable (By Iris) [video]
(10 June 2021) Chapter 7: Empirical Processes (by Martin) [video]
(15 June 2021) Chapter 8: Martingales (by Di) [video]
(17 June 2021) Chapter 9: Time and Spatial Series (by Zerun) [video]
(24 June 2021) Chapter 10: Stochastic Processes (by Dominic) [video]
(6 July 2021) Chapter 11: Nonparametric Statistics (by Lexi) [video]
(8 June 2021) Chapter 12: Mixed Effects Models (by Andy) [video]
(13 June 2021) Chapter 13: Small-Area Estimation (by Billy) [video]
(15 June 2021) Chapter 14: Jackknife and Bootstrap (by Ben) [video]
(20 June 2021) Chapter 15: Markov-Chain Monte Carlo (by Heman) [video]
Part (II): Reading the state-of-the-art methods
Multiscale inference and long-run variance estimation in non-parametric regression with time series errors (Khismatullina and Vogt, 2019)
Stochastic Gradient Markov Chain Monte Carlo (Nemeth and Fearnhead, 2021)
Count Time Series: A Methodological Review (Davis, et.al, 2021)
On bandwidth choice for spatial data density estimation (Jiang, et.al, 2020)
Detecting Abrupt Changes in the Presence of Local Fluctuations and Autocorrelated Noise (Romano, et,al, 2021)
Functional time series prediction under partial observation of the future curve (Jiao, Aue and Ombao, 2021)
Finite sample change point inference and identification for high-dimensional mean vectors (Yu and Chen, 2020)
A Causal Bootstrap (Imbens and Menzel, 2021+)
Doubly robust inference when combining probability and non-probability samples with high dimensional data (Yang, Kim and Song, 2020)
Missing at random: a stochastic process perspective (Farewell, Daniel and Seaman, 2021)
Part (III): Research presentations
#5 Probability with Martingale (2019-20 Summer)
We will read the book Probability with Martingale by David Williams, some recent research articles (related to missing data and time series). We will present our own research projects as well.
Part (I): Reading Williams
(2 Jun 2020): Sensitivity model (by Iris), Chapter 0 Branching process (by Keith) [Video]
(4 Jun 2020): Chapter 1 measure spaces (by Keith), Chapter 2 Events (by James) [Video]
(9 Jun 2020): Chapter 3 random variables (by James) [Video]
(11 Jun 2020): Chapter 4 independence (by Zerun) [Video], and Chapter 5 integral (by Di) [Video]
(16 Jun 2020): Chapter 6 expectation and Chapter 7 An Easy strong law (by Dominic) [Video]
(18 Jun 2020): Chapter 8 product measure (By Andy), and Chapter 9 conditional expectation (by Ben) [Video]
(24 Jun 2020): Chapter 10 martingales (by Iris), and Chapter 11 the convergence theorem (by Lexi) [Video]
(25 Jun 2020): Advances in multiple imputation (by Tom) [Video], and time series with missing data (by Ian) [Video]
(30 Jun 2020): Chapter 12 martingales bounded in L2, and Chapter 13 Uniform integrability (by Heman) [Video]
(2 Jul 2020): Chapter 14 UI martingales (by Martin and Dingdong) [Video]
(7 Jul 2020): Chapter 15 applications (by Billy and Adrian) [Video of part I] [Video of part II]
(9 Jul 2020): Chapter 16 basic properties of CFs, Chapter 17 weak convergence, and Chapter 18 the central limit theorem (by Ian and Tom) [Video]
Part (II): Reading the state-of-the-art methods
(9 Jul 2020): A robust method for shift detection in time series (Dehling & Wendler 2020) [Video]
(14 Jul 2020): Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes (Chernozhukov, et.al 2020) [Video]
(21 Jul 2020): Sensitivity Analysis for Unmeasured Confounding in Meta Analyses (Mathur and Tyler 2020) [Video]
(23 Jul 2020): Nonparametric Imputation by Data Depth (Mozharovskyi, Josse, Husson, 2020) [Video]
(4 Aug 2020) Causal isotonic regression (Westling, Gilbert, Carone, 2020) [Video]
(6 Aug 2020) Dynamic models for spatiotemporal data (Stroud, Muller, Sanso, 2001) [Video]
(11 Aug 2020) Power and Bipower Variation with Stochastic Volatility and Jumps (Barndorff-Nielsen & Shephard 2004) [Video]
Part (III): Research presentations
#4 Asymptotic Theory in Time Series (2019-20 Spring)
We will read the following papers written by Wei Biao Wu.
Asymptotic theory for stationary processes
And many related articles here: http://www.stat.uchicago.edu/~wbwu/papers.html
Recursive estimation of time-average variance constants; see also some related papers written by me:
The reading videos can be found below.
Reading class 1 (3 April 2020): definition of dependence measure, some examples, etc.
Reading class 2 (10 April 2020): strong invariance principle, spectral density, kernel estimator, U-statistics, etc [Video]
Reading class 3 (17 April 2020): recursive estimator, proof of consistency, etc [Video]
Reading class 4 (26 May 2020): recursive estimator, convergence rate, etc [Video]
Reading class 5 (18 May 2020): recursive estimator, convergence rate, etc [Video]
#3 Large Scale Inference (2019-20 Fall)
We will read Large-Scale Inference by Efron.
#2 Asymptotic Statistics (2018-19 Summer)
It is a reading group for postgraduate students. We will
read recent research articles on missing data and change point detection;
learn/review standard results in asymptotic statistics; and
present our recent research progress.
The class will mainly follow the topics covered in the book Asymptotic Statistics written by A. W. van der Vaart.
Time: M&H 4:00-6:30
Location: conference room (LSB G21)
#1 Statistical Machine Learning (2018-19 Fall)
It is a reading group for postgraduate students. I will
review some basic statistics topics; and
teach and discuss some basics and hot topics in statistical learning.
The class will mainly follow the topics covered in the statistical machine learning course taught by Larry (one of my favorite professors!) at CMU. Selected papers will also be presented.
Time: W 4:30-6:30
Location: conference room (LSB G21)