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D-Index & Metrics

Mathematics

D-Index
58
Citations
33951
World Ranking
608
National Ranking
306

Research.com Recognitions

  • 2006 - Fellow of the American Statistical Association (ASA)
  • 1994 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 1987 - Fellow of John Simon Guggenheim Memorial Foundation

Overview

Jon A. Wellner is affiliated with the University of Washington in the United States and has contributed extensively to the field of mathematics. Their research primarily focuses on areas within statistics and probability, applied mathematics, and artificial intelligence, alongside topics in management science, operations research, and finance.

Their work spans several main topics including statistical methods and inference, Bayesian methods and mixture models, statistical methods and Bayesian inference, point processes and geometric inequalities, advanced harmonic analysis research, random matrices and applications, as well as Markov chains and Monte Carlo methods.

Wellner has published in multiple academic venues. Frequent publication venues include:

  • The Annals of Statistics
  • arXiv (Cornell University)
  • Apollo (University of Cambridge)
  • Electronic Journal of Probability
  • Statistics & Probability Letters

Recent papers encompass:

  • Complex sampling designs: Uniform limit theorems and applications (2021, The Annals of Statistics)
  • Bounding distributional errors via density ratios (2020, Apollo (University of Cambridge))
  • Hardy's inequality and its descendants: a probability approach (2021, Electronic Journal of Probability)
  • Stein 1956: Efficient nonparametric testing and estimation (2021, The Annals of Statistics)
  • The density ratio of Poisson binomial versus Poisson distributions (2020, Statistics & Probability Letters)

Frequent collaborators in Wellner's work include Aad van der Vaart, Lutz Dümbgen, Chris A. J. Klaassen, Nilanjana Laha, and Qiyang Han.

Wellner has contributed to scholarly literature beyond articles, including book publications. Notably, a book titled Weak Convergence and Empirical Processes was published by Springer Science+Business Media in 2023, which has accumulated more than 100 citations.

Throughout their career, Wellner has been recognized by various professional societies. They were named a Fellow of the American Association for the Advancement of Science (AAAS) in 1994, a Fellow of the John Simon Guggenheim Memorial Foundation in 1987, and a Fellow of the American Statistical Association (ASA) in 2006.

Best Publications

  • Weak Convergence and Empirical Processes: With Applications to Statistics

    Jon Wellner

  • Weak Convergence and Empirical Processes

    Thomas Mikosch;Aad W. Van der Vaart;Jon A. Wellner

  • Empirical processes with applications to statistics

    Galen R. Shorack;Jon A. Wellner

  • Efficient and Adaptive Estimation for Semiparametric Models.

    K. A. Do;P. J. Bickel;C. A. J. Klaassen;Y. Ritov

  • Information Bounds and Nonparametric Maximum Likelihood Estimation

    Piet Groeneboom;Jon A. Wellner

  • Information and Asymptotic Efficiency in Parametric-Nonparametric Models

    Janet M. Begun;W. J. Hall;Wei-Min Huang;Jon A. Wellner

  • Non- and semi-parametric maximum likelihood estimators and the von Mises method. II

    R. D. Gill;J. A. Wellner

  • Confidence bands for a survival curve from censored data

    W. J. Hall;Jon A. Wellner

  • Estimation of a convex function: characterizations and asymptotic theory.

    Piet Groeneboom;Geurt Jongbloed;Jon A. Wellner

  • Large Sample Theory of Empirical Distributions in Biased Sampling Models

    Richard D. Gill;Yehuda Vardi;Jon A. Wellner

  • Interval Censored Survival Data: A Review of Recent Progress

    Jian Huang;Jon A. Wellner

  • Exchangeably Weighted Bootstraps of the General Empirical Process

    Jens Praestgaard;Jon A. Wellner

  • A Hybrid Algorithm for Computation of the Nonparametric Maximum Likelihood Estimator from Censored Data

    Jon A. Wellner;Yihui Zhan

  • High Dimensional Probability Ii

    Evarist Giné;David M. Mason;Jon A. Wellner

  • Log-Concavity and Strong Log-Concavity: a review.

    Adrien Saumard;Jon A. Wellner

  • Efficient estimation in the bivariate normal copula model: normal margins are least favourable

    Chris A.J. Klaassen;Jon A. Wellner

  • Two estimators of the mean of a counting process with panel count data

    Jon A. Wellner;Ying Zhang

  • Weighted Likelihood for Semiparametric Models and Two-phase Stratified Samples, with Application to Cox Regression.

    Norman E. Breslow;Jon A. Wellner

  • Limit theorems for the ratio of the empirical distribution function to the true distribution function

    Jon A. Wellner

  • TWO LIKELIHOOD-BASED SEMIPARAMETRIC ESTIMATION METHODS FOR PANEL COUNT DATA WITH COVARIATES

    Jon A. Wellner;Ying Zhang

  • Large sample theory of empirical distributions in biased sampling models

    Richard Gill;J.A. Wellner

Frequent Co-Authors

Aad van der Vaart
Aad van der Vaart Delft University of Technology
Piet Groeneboom
Piet Groeneboom Delft University of Technology
Jian Huang
Jian Huang University of Iowa
Norman E. Breslow
Norman E. Breslow University of Washington
Richard D. Gill
Richard D. Gill Leiden University
Ya'acov Ritov
Ya'acov Ritov University of Michigan–Ann Arbor
Peter J. Bickel
Peter J. Bickel University of California, Berkeley
David M. Mason
David M. Mason University of Delaware
Vladimir Koltchinskii
Vladimir Koltchinskii Georgia Institute of Technology

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