**R.R. Bahadur's Lectures on the Theory of Estimation**

By Raghu Raj Bahadur, Stephen M. Stigler,

Wing Hung Wong, and Daming Xu

**LECTURE 21 : April 27 and 30, 2020 - ****POSTPONED**

SARA VAN DE GEER, Department of Mathematics, ETH Zürich

"Total Variation Regularization Part 1"

"Total Variation Regularization Part 2"

There will be two lectures. In the first we present a general overview concerning adaptive estimation using total variation regularization. Our main tool will consist of so-called "interpolating vectors" which we introduce in the first lecture for the noiseless case. In the second lecture, we discuss the noisy case in more detail for some special cases. The second lecture will not require knowledge from the first one.

**LECTURE 20 : May 6 and 9, 2019**

IAIN JOHNSTONE, Departments of Statistics, Health Research and Policy, Stanford University

"High Dimensional Classical Multivariate Analysis: Ladders and Local Asymptotic Normality"

"High Dimensional Principal Component Analysis: Biases and Balms."

**LECTURE 19: April 16 and 19, 2018**

HÅVARD RUE, Departments of Statistics and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology

"Some New Developments in the R-INLA Project"

"Penalizing Model Component Complexity: A Principled Practical Approach to Constructing Priors"

**LECTURE 18: April 19 and 20, 2017**

XIAO-LI MENG, Department of Statistics, Harvard University

"There is Individualized Treatment. Why Not Individualized Inference?"

"From Eckhart Hall to (almost) White House: An Unexpected Statistical Journey (Or: How small are my big data?)"

**LECTURE 17: May 11 and 13, 2016**

DONALD GEMAN, Department of Applied Mathematics and Statistics, Johns Hopkins University

"Designing Vision Machines by Entropy Pursuit"

"Testing Vision Machines by Entropy Pursuit"

MICHAEL I. JORDAN, Department of Statistics, University of California, Berkeley

"Distributed Computing, the Bootstrap, and Concurrency Control"

"On Computational Thinking, Inferential Thinking, and 'Big Data'"

**LECTURE 15: April 2 and 3, 2014**

SUSAN A. MURPHY, Department of Statistics, University of Michigan, Ann Arbor

"Machine Learning Methods for Individualizing Just in Time Adaptive Interventions"

"Getting SMART about Adapting Interventions"

**LECTURE 14: April 15 and 18, 2013**

PETER J. GREEN, University of Bristol and University of Technology, Sydney

"Emission Tomography and Bayesian Inverse Problems"

"Bayesian Graphical Model Determination"

**LECTURE 13: April 16 and 17, 2012**

PETER BÜHLMANN, Seminar für Statistik, ETH Zürich

"Assigning Statistical Significance in High-Dimensional Problems"

"Causal Statistical Inference and Intervention Experiments for Large-Scale Biological Systems"

**LECTURE 12: April 11 and 14, 2011**

JAMES O. BERGER, Department of Statistical Science, Duke University

"Bayesian Adjustment for Multiplicity"

"I don't know where I'm gonna go when the volcano blows"

**LECTURE 11: November 9 and 12, 2009**

PETER HALL, Department of Mathematics and Statistics, University of Melbourne, Australia

"Modelling the Variability of Rankings"

"Contemporary Frontiers in Statistics"

**LECTURE 10: May 18 and 21, 2009**

STEFFEN LAURITZEN, Department of Statistics, University of Oxford

"Sufficiency and Transitivity"

"Bayesian Networks for the Analysis of DNA Mixtures"

STUART GEMAN, Division of Applied Mathematics, Brown University

"Rare Events in the Financial Markets"

"On the Peculiar Statistics of Natural Images"

**LECTURE 8: March 26 and 29, 2007**

WING H. WONG, Department of Statistics, Stanford University

"Statistical Issues in the Study of Gene Regulation"

"Learning Causal Bayesian Network Structures from Experimental Observations"

ELIZABETH A. THOMPSON, Departments of Statistics, and of Genome Sciences, University of Washington

"Monte Carlo Likelihood Inference in Latent Variable Problems"

"Uncertainty and Evidence in the Face of Unseen Data"

**LECTURE 6: May 16 and 18, 2005**

WILLEM R. VAN ZWET, Mathematical Institute, University of Leiden

"Statistics and the Law: The Case of the Negligent Nurse"

"Kakutani’s Interval Splitting Scheme"

**LECTURE 5: October 16 and 17, 2003**

DAVID O. SIEGMUND, Department of Statistics, Stanford University

"Statistical Problems of Genetic Mapping"

"Gene Mapping and Model Selection"

**LECTURE 4: May 17 and 20, 2002**

ADRIAN BADDELEY, Department of Mathematics and Statistics, University of Western Australia

"Counting Leaves on a Tree and Neurons in the Brain"

"Practical Maximum Pseudolikelihood for Spatial Data"

PETER J. BICKEL, Department of Statistics, University of California, Berkeley

"Suggestive Statistics and Texture Analysis"

"Testing Semiparametric Hypotheses and Unorthodox Bootstraps"

**LECTURE 2: May 23 and 24, 2000**

PERSI DIACONIS, Department of Statistics, Stanford University

"On Coincidences"

LAWRENCE D. BROWN, Department of Statistics, University of Pennsylvania

"Current Plans and Prospects for Census 2000"

"New, Improved Confidence Intervals for a Binomial Proportion"