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
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.
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."
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"
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?)"
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'"
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"
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"
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"
STEFFEN LAURITZEN, Department of Statistics, University of Oxford
"Sufficiency and Transitivity"
"Bayesian Networks for the Analysis of DNA Mixtures"
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"
WILLEM R. VAN ZWET, Mathematical Institute, University of Leiden
"Statistics and the Law: The Case of the Negligent Nurse"
"Kakutani’s Interval Splitting Scheme"
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"
PERSI DIACONIS, Department of Statistics, Stanford University
LAWRENCE D. BROWN, Department of Statistics, University of Pennsylvania
"Current Plans and Prospects for Census 2000"
"New, Improved Confidence Intervals for a Binomial Proportion"