Robert J. Buehler (1925-1988) was born in Alma, Wisconsin, and earned his PhD at University of Wisconsin, Madison. Having worked at Sandia Labs and the Universities of Wisconsin and Iowa, he came to the U in 1963 and served the School in several capacities including a spell as Department Chair.
Bob was an active thesis adviser, and is remembered as a devoted teacher and an outstanding scholar. His research area was inference: ancillary statistics, pivotals and fiducial probability were of particular interest. His 1976 incoherence example http://www.jstor.org/stable/2958578 and the 1967 “best invariant predictor” paper with Hora http://www.jstor.org/stable/2238996 are landmarks in the area.
Colleagues recall that he was a health nut who played tennis and logged 10,000 jogging miles. He was also an avid chess player.
Bob married Barbara Martin in 1965, and the couple had three children.
The annual Buehler-Martin Lectureship was created by the Martin family after Bob’s death to honor his memory. Under the program, each year we invite a distinguished statistician to spend a week with us giving three lectures. One lecture is less technical and appropriate for an audience wider than just statisticians.
Department of Statistics, University of Michigan
|3/29: From Statistical downscaling to Multivariate Quantiles
3/31: Model-Based Inference in Subgroup Analysis
|2015||Lynn Y.S. Lin
Lynn Y.S. Lin Statistical Consulting, Inc.
|3/25: How Did I Get Into New Product Sales Forecasting Business?
Penn State University
|3/10: Mixture models: the data story, the mysteries, and the surprises.
3/12: Sufficient projections through a Fisherian information matrix
University of Wisconsin, Madison
|3/26: Why Don't We Agree? Studying Influenza with RNA Interference
3/27: Decoding Functional Signals with the Role Model
3/28: Probabilities Over Ranks of Gamma or Normal Random Variables
|2011 (F)||Adrian Raftery
Professor of Statistics and Sociology University of Washington
|10/25: Fast Inference for Model-Based Clustering of Networks Using an Approximate Case-Control Likelihood
10/26: Probabilistic Projection of Life Expectancy for All Countries to 2100
10/27: Probabilistic Weather Forecasting Using Ensemble Bayesian Model Averaging
|2011 (Sp)||Jianqing Fan
|4/19: A Statistician's Guide to Vast-dimensional Space
4/20: Refitted Cross-validation in Ultrahigh Dimensional Regression
4/21: Control of the False Discovery Rate Under Arbitrary Covariance Dependence
University of California at Berkeley
|4/12: Measuring Traffic
4/14: Statistics of the Taiwanese-American Occultation Survey
4/15: Searching for Gamma-Ray Pulsars: Detecting Periodicity in a Point Process
|3/9: Modern Trends in Data Mining
3/10: Regularization Paths and Coordinate Descent (joint work with Jerome Friedman and Rob Tibshirani)
|2008||Lawrence D. Brown
Wharton School University of Pennsylvania
|4/29: In-Season Prediction of Batting Averages: A Field-test of Basic Empirical Bayes and Bayes Methodologies
4/30: Non-parametric Empirical Bayes and Compound Bayes Estimation of Independent Normal Means
5/1: A Root-Unroot Algorithm for Nonparametric Density Estimation and an Implementation via Adaptive Wavelet Block Threshholding
University of Glasgow, Scotland, UK
|4/10: Pearson the Elder - A Statistical Giant
4/11: Variational approximations in incomplete-data problems
4/12: Bayesian measures of complexity, and model selection,based on incomplete data
University of Chicago
|3/20: Some Remarks About Spatial Correlation of Crop Yields
3/22: Partition Models and Cluster Processes
3/23: Random Partitions and Logistic Classification
|2005||Raymond J. Carroll
Texas A & M
|3/29: Measuring Diet: Is it Possible?
3/30: Longitudinal and Clustered Data and Non/Semiparametric Regression
3/31: Semiparametric Methods for Gene-environment Case-control Studies When Gene and Environment Are Independent in the Population
University of Washington
|4/19: Inferring Relationships Among Populations and Individuals
4/20: The Structure of Pedigree Data and the Detection of Linkage
4/22: Monte Carlo Likelihood in Genetic Mapping
Australian National University
|4/29: Nonparametric Methods for Estimating Light Curves for Periodic Variable Stars
4/30: Statistical Inference in High-Dimensional, Low Sample Size Settings
5/1: Testing for Equality of Distributions in Very High Dimensions
University of California
|4/16: Statistical Issues in Census 2000
4/17: The Swine Flu Vaccine and Guillain-Barre syndrome
4/19: Salt and Blood Pressure: Conventional Wisdom Reconsidered
|1999(Sp)||James O. Berger
University of Wisconsin
London School of Economics, UK
University of Virginia
University of Cambridge, UK