Overview

Welcome to our repository compiled of seminal papers in the field of statistics. This website is the culmination of work by Stanford statistics PhD students in Rob Tibshirani’s Statistics 319 literature class. Each week we presented a different paper to our peers which led to thoughtful discussions and a concise written summary designed to facilitate a deeper understanding of complex concepts.

Our objective is to make this body of knowledge accessible to undergradate students in hopes of capturing their interest and convincing students to pursue a education and career in statistics.

Author	Paper
Nan Laird	Random-Effects Models for Logitudinal Data
Yoav Benjamini and Yosef Hochberg	Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Leo Breiman	Statistical Modeling: The Two Cultures
Brad Efron	Bootstrap Methods: Another Look at the Jackknife
Art Dempster, Nan Laird, and Donald Rubin	Maximum Likelihood from Incomplete Data via the EM Algorithm
Leonard Baum and Lloyd Welch	Baum-Welch Algorithm
Alan Gelfand and Sir Adrian Smith	Sampling-Based Approaches to Calculating Marginal Densities
Sir David Cox	Regression Models and Life-Tables
Paul Rosenbaum and Donald Rubin*	The Central Role of the Propensity Score in Observational Studies for Causal Effects
Trevor Hastie and Robert Tibshirani*	Generalized Additive Models
Vladimir Vovk*	A Tutorial on Conformal Prediction
Jerry Friedman*	Greedy Function Approximation: A Gradient Boosting Machine
Jamie Robins*	Association, Causation, and Marginal Structural Models
Rob Tibshirani*	Regression Shrinkage and Selection via the Lasso

* There are no slides or summary available for these papers because we did not get a chance to cover them in class.