From Probability to Statistics and Back: High-Dimensional Models and Processes -- A Festschrift in Honor of Jon A. Wellner
Editor: M. Banerjee
Editor: F. Bunea
Editor: J. Huang
Editor: V. Koltchinskii
Editor: M. H. Maathuis
Collections, Volume 9
Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2013.
For more than thirty years, Jon A. Wellner has made outstanding contributions to several very active and important areas of statistics and probability. His results have been especially influential in semiparametric statistics, estimation and testing problems under shape constraints, empirical processes theory (both classical and abstract), survival analysis, biostatistics, bootstrap, probability in Banach spaces and high-dimensional probability. Among the main features of Jon’s work are his exceptional taste and ability to identify research problems in statistics that are both challenging and important, his deep understanding of the purely mathematical side of statistics, his extraordinary curiosity and interest in the work of others and the quality of his insights.
Jon’s contribution to the statistical arena is further underscored by his four highly influential (co-authored) books on empirical processes, semi-parametric models and nonparametric maximum likelihood estimation. The impact of his books on the discipline and the vital role that they played in communicating the power of empirical processes and semiparametric theory to the statistical community as effective tools for studying statistical models can hardly be exaggerated. Indeed, in this regard, he should be seen as one of the visionaries who helped unleash the potency of empirical process theory for solving hard theoretical problems in the statistical arena and which brought about a paradigm shift in the approach to a broad sphere of asymptotics.
Jon has also been a prolific mentor with 27 graduated Ph.D. students (and one more being advised) at the time of going to press, many of whom have gone on to successful research careers at distinguished universities. In addition, he has been a mentor and source of inspiration to junior colleagues who were not his students and who, in many cases, are formidable names in the profession today. Further evidence of Jon’s influence on colleagues and students is furnished by the fact that he was recently appointed as Knight in the Order of the Netherlands Lion (in July 2010) in recognition of his impact on the development of stochastic sciences in the Netherlands.
It is, therefore, both a pleasure and privilege for us to present this Festschrift in honor of Jon’s 65th birthday. Many of the papers included in this volume were presented at the conference From Probability to Statistics and Back: High-Dimensional Models and Processes that took place in Seattle, Washington on July 28 - 31, 2010. They cover a broad range of topics related, at various levels, to Jon’s work. We would like to take this opportunity to extend our most sincere thanks to the contributors of this volume. On behalf of the participants of the aforementioned conference in which the seeds of this volume were sown, we would like to acknowledge the support of National Science Foundation, University of Washington and the local organizers in Seattle, especially Petra Buzkova and Arseni Seregin. We also thank Shawn Mankad for helping us extensively with the tex work that was needed to put the volume together. Last but not least, we thank Aurore Delaigle and Elyse Gustafson at IMS for working closely with us to produce this volume.
In conclusion, we would like to express our deep appreciation for Jon Wellner, in our various capacities as students and collaborators and wish him many more happy and productive years ahead.
Copyright © 2010, Institute of Mathematical Statistics.
A counterexample concerning the extension of uniform strong laws to ergodic processes
Terrence Adams, and Andrew Nobel; 1-4
Mechanical models in nonparametric regression
Vladimír Balek, and Ivan Mizera; 5-19
Efficient estimation in the semiparametric normal regression-copula model with a focus on QTL mapping
Bojan Basrak, and Chris A. J. Klaassen; 20-32
Multivariate regression through affinely weighted penalized least squares
Rudolf Beran; 33-46
Semiparametric models and two-phase samples: Applications to Cox regression
Norman E. Breslow, and Thomas Lumley; 65-77
Stochastic search for semiparametric linear regression models
Lutz Dümbgen, Richard J. Samworth, and Dominic Schuhmacher; 78-90
On low-dimensional projections of high-dimensional distributions
Lutz Dümbgen, and Perla Del Conte-Zerial; 91-104
On asymptotic quantum statistical inference
Richard D. Gill, and Mădălin I. Guţă; 105-127
On the estimation of smooth densities by strict probability densities at optimal rates in sup-norm
Evarist Giné, and Hailin Sang; 128-149
Yair Goldberg, Rui Song, and Michael R. Kosorok; 150-162
Consistent scoring functions for quantiles
Kyrill Grant, and Tilmann Gneiting; 163-173
Smooth and non-smooth estimates of a monotone hazard
Piet Groeneboom, and Geurt Jongbloed; 174-196
Efficient testing and estimation in two Lehmann alternatives to symmetry-at-zero models
W. J. Hall, and Jon A. Wellner; 197-212
A remark on low rank matrix recovery and noncommutative Bernstein type inequalities
Vladimir Koltchinskii; 213-226
Analyzing posteriors by the information inequality
Willem Kruijer, and Aad van der Vaart; 227-240
Uniform in bandwidth limit laws for kernel distribution function estimators
David M. Mason, and Jan W. H. Swanepoel; 241-253
Around Nemirovski’s inequality
Pascal Massart, and Raphaël Rossignol; 254-265
A note on insufficiency and the preservation of Fisher information
David Pollard; 266-275
Improved matrix uncertainty selector
Mathieu Rosenbaum, and Alexandre B. Tsybakov; 276-290
Some asymptotic theory for functional regression with stationary regressor
Frits Ruymgaart, Jing Wang, and Shih-Hsuan Wei; 291-302
The Lasso, correlated design, and improved oracle inequalities
Sara van de Geer, and Johannes Lederer; 303-316