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Mathematics

D-Index
70
Citations
26098
World Ranking
270
National Ranking
149

Research.com Recognitions

  • 1989 - Fellow of the American Statistical Association (ASA)

Overview

David Ruppert is affiliated with Cornell University in the United States. Their research primarily focuses on mathematics, with significant contributions in statistics and probability. Their work also spans related areas including artificial intelligence, molecular biology, finance, and control and systems engineering.

Their research topics include:

  • Statistical Methods and Inference
  • Advanced Statistical Methods and Models
  • Statistical Methods and Bayesian Inference
  • Bayesian Methods and Mixture Models
  • Control Systems and Identification
  • Gaussian Processes and Bayesian Inference
  • Spectroscopy and Chemometric Analyses

Recent publications by Ruppert feature a range of statistical and computational topics. Notable papers include:

  • Optimal Sampling for Generalized Linear Models Under Measurement Constraints, 2020, Journal of Computational and Graphical Statistics
  • Proteomics and Cytokine Analyses Distinguish Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Cases from Controls, 2023, Journal of Translational Medicine
  • Bootstrap Inference for Quantile-based Modal Regression, 2021, Journal of the American Statistical Association
  • Model Checking for Logistic Models When the Number of Parameters Tends to Infinity, 2022, Journal of Computational and Graphical Statistics
  • A Semiparametric Risk Score for Physical Activity, 2021, Statistics in Medicine

Frequent collaborators of Ruppert include:

  • Tao Zhang
  • Hua Liang
  • Tui H. Nolan
  • Jeff Goldsmith
  • Mohammad W. Hattab

Ruppert's research has been published regularly in venues such as:

  • arXiv (Cornell University)
  • Journal of Computational and Graphical Statistics
  • Journal of the American Statistical Association
  • Statistica Sinica
  • UNC Libraries

David Ruppert was recognized as a Fellow of the American Statistical Association (ASA) in 1989.

Best Publications

  • Transformation and Weighting in Regression

    R. J. Carroll;D. Ruppert

  • Measurement Error in Nonlinear Models

    Raymond J. Carroll;David Ruppert;Leonard A. Stefanski

  • Multivariate Locally Weighted Least Squares Regression

    D. Ruppert;M. P. Wand

  • An Effective Bandwidth Selector for Local Least Squares Regression

    D. Ruppert;S. J. Sheather;M. P. Wand

  • Selecting the Number of Knots for Penalized Splines

    David Ruppert

  • Trimmed Least Squares Estimation in the Linear Model

    David Ruppert;Raymond J. Carroll

  • Signal-to-noise ratios, performance criteria, and transformations

    George Box;Anne C. Shoemaker;Kwok-Leung Tsui;Ramon V. León

  • Penalized Spline Estimation for Partially Linear Single-Index Models

    Yan Yu;David Ruppert

  • Statistics and Data Analysis for Financial Engineering

    David Ruppert

  • A Note on Computing Robust Regression Estimates via Iteratively Reweighted Least Squares

    James O. Street;Raymond J. Carroll;David Ruppert

  • Likelihood ratio tests in linear mixed models with one variance component

    Ciprian M. Crainiceanu;David Ruppert

  • Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition

    Raymond J. Carroll;David Ruppert;Leonard A. Stefanski;Ciprian M. Crainiceanu

  • Regression depth. Commentaries. Rejoinder

    P. J. Rousseeuw;M. Hubert;X. He;R. Koenker

  • Bayesian Analysis for Penalized Spline Regression Using WinBUGS

    Ciprian M. Crainiceanu;David Ruppert;Matthew P. Wand

  • Efficient Estimations from a Slowly Convergent Robbins-Monro Process

    D. Ruppert

  • Transformations in Density Estimation

    M. P. Wand;J. S. Marron;D. Ruppert

  • Fitting a Bivariate Additive Model by Local Polynomial Regression

    Jean D. Opsomer;David Ruppert

  • Robust Estimation in Heteroscedastic Linear Models.

    Raymond J. Carroll;David Ruppert

  • Theory & Methods: Spatially‐adaptive Penalties for Spline Fitting

    David Ruppert;Raymond J. Carroll

  • The Elements of Statistical Learning: Data Mining, Inference, and Prediction

    David Ruppert

  • Semiparametric Regression: Preface

    David Ruppert;M. P. Wand;R. J. Carroll

Frequent Co-Authors

Raymond J. Carroll
Raymond J. Carroll Texas A&M University
Matt P. Wand
Matt P. Wand University of Technology Sydney
Leonard A. Stefanski
Leonard A. Stefanski North Carolina State University
Christine A. Shoemaker
Christine A. Shoemaker National University of Singapore
Hua Liang
Hua Liang George Washington University
James Stephen Marron
James Stephen Marron University of North Carolina at Chapel Hill
Giles Hooker
Giles Hooker University of Pennsylvania
Maureen R. Hanson
Maureen R. Hanson Cornell University
Edward W. Frees
Edward W. Frees University of Wisconsin–Madison
Dan Nettleton
Dan Nettleton Iowa State University

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