This is the webpage of the Computational Statistics Reading group, IIT Kanpur. The group meets to discuss new and old works in the broad area of Computational Statistics.

Broad topics of interest include (but are not limited to):

  • Statistical Optimization
  • Monte Carlo
  • Bayesian computation
  • Bootstrap
  • Cross-validation
  • Density estimation
  • Visualization
  • Reproducibility

Prerequisites: Although coding will not be a primary component of the meetings, whenever needed we may discuss coding implementation issues in R. Additionally, a basic understanding of Markov chains, optimization methods, and broad-spectrum statistics will be helpful.

The group meets every two weeks on Thursdays at 3pm in FB567 to discuss new and old works in this area. For information on joining these meetings, please contact dootika@iitk.ac.in


Schedule for Semester II 2022-2023:

Date Paper/Topic Led By Resource
Jan 19 EVA2021 Challenge Arnab Hazra arxiv
Feb 2 (at 2pm!) Population Modeling Subject to Binomial Catastrophes Nitin Kumar Paper 1, Paper 2
Feb 16 Stochastic Gradient MCMC Apratim Shukla  
Mar 2 Nonconvex optimization using Nelder-Mead algorithm Anjali Mittal  
Mar 16 meeting cancelled Arghya Mukherjee  
Mar 30 Holiday – no meeting    
Apr 13 Randomized and Exchangeable Improvements of Markov’s, Chebyshev’s and Chernoff’s Inequalities Dootika Vats  

Schedule for Semester I 2022-2023:

Date Paper/Topic Led By Resource
Aug 2 Metropolis-Adjusted Langevin Algorithm Dootika Vats Exponential Convergence of Langevin Distribution and Their Discrete Approximations by Roberts and Tweedie (1996)
Aug 16 Estimation using Generalised estimating equations (GEE) and it’s application in crossover design Satya Singh Longitudinal Data Analysis Using Generalized Linear Models
Aug 30 Stein’s method and approximations by the Normal distribution Suprio Bhar A short survey of Stein’s Method, Fundamentals of Stein’s Method
Sept 13 Stein’s Method for Computational Statistics Rahul Singh Stein’s Method meets Computational Statistics, Variance reduction in MCMC
Sept 27 Second-order Stein Arka Banerjee Second-order Stein: SURE for SURE and other applications in high-dimensional inference by Bellec and Zhang
Oct 11 No meeting    
Oct 25 No meeting    
Nov 1 Bernstein-von Mises theorem Minerva Mukhopadhyay Book chapter: Asymptotic Statistics by van der Vaart, On the BvM Theorem with Infinite-Dimensional Parameters

Archive:

Date Paper/Topic Led By Resource
25/03/22 Multivariate Quantiles Subhra Sankar Dhar Notes
04/03/22 Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations Arnab Hazra  
18/02/22 Statistical inference for model parameters in stochastic gradient descent by Chen et. al. Rahul Singh slides
11/02/22 Introduction to Stochastic Gradient Descent, Paper: Robbins and Monro, Polyak and Juditsky Abhinek Shukla -
28/01/22 “Density Estimation for the Metropolis-Hastings Algorithm” by Sköld and Roberts Dootika Vats Codes
03/11/21 “Cross-validation: what does it estimate and how well does it do it?” by Bates, Hastie, Tibshirani Dootika Vats Codes
26/10/21 Numerical aspects of Stochastic Differential Equations Akash Anand Slides, Codes
19/10/21 Numerical aspects of Stochastic Differential Equations Akash Anand - Numerical Solution of Stochastic Differential Equation by Peter E. Kloeden and Eckhard Platen
05/10/21 Motivating Stochastic Differential Equations - 2 Suprio Bhar Stochastic Differential Equations by Bernt Oksendal (Chapter 1), Foundations of the Theory of Semilinear Stochastic Partial Differential Equations
28/09/21 Motivating Stochastic Differential Equations - 1 Suprio Bhar Notes on Brownian Motion by Curien Nicolas, Stochastic Differential Equations by Bernt Oksendal (Chapter 1)
21/09/21 “Fast sampling with Gaussian scale mixture priors in high-dimensional regression”(2016) by Anirban Bhattacharya, Antik Chakraborty, Bani K. Mallick Online Dootika Vats Simulations
07/09/21 “Some Asymptotic Theory for the Bootstrap” by Bickel and Freedman (1981) Online Abhinek Shukla and Rahul Singh Slides 1 Slides 2
24/08/21 “What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum” by Tim Hesterberg (2015)Online Dootika Vats and Agamani Saha R code