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David Ruppert
Researcher at Cornell University
Publications - 256
Citations - 30792
David Ruppert is an academic researcher from Cornell University. The author has contributed to research in topics: Estimator & Nonparametric regression. The author has an hindex of 61, co-authored 252 publications receiving 27137 citations. Previous affiliations of David Ruppert include University of Vermont & University of North Carolina at Chapel Hill.
Papers
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Journal ArticleDOI
Hierarchical Adaptive Regression Kernels for Regression With Functional Predictors
TL;DR: A new method for regression using a parsimonious and scientifically interpretable representation of functional predictors is proposed, and it is shown that the method is more effective and efficient for data that include features occurring at varying locations.
Journal ArticleDOI
Uncertainty Analysis for Computationally Expensive Models with Multiple Outputs
TL;DR: Bayesian MCMC calibration and uncertainty analysis for computationally expensive models is implemented using the SOARS (Statistical and Optimization Analysis using Response Surfaces) methodology and enhancements of the GRIMA algorithm were introduced to improve efficiency.
Book ChapterDOI
Improving MCMC Mixing for a GLMM Describing Pathogen Concentrations in Water Supplies
TL;DR: Different possible models are discussed considering alternative covariates and their appropriateness for forecasting, and for analyzing risks from long-term exposure, and a fully Bayesian approach using MCMC simulation is used for model inference.
Journal ArticleDOI
Nonparametric Regression and Spline Smoothing
TL;DR: In this article, nonparametric regression and spline smoothing are used to solve the problem of Spline Smoothing in the non-parametric Regression problem. But
Journal ArticleDOI
Latent factor regression models for grouped outcomes.
Dawn B. Woodard,Tanzy Love,Sally W. Thurston,David Ruppert,Sheela Sathyanarayana,Shanna H. Swan +5 more
TL;DR: A set of identifiable models along a spectrum between parsimonious random effect multiple outcomes models and more general continuous latent factor models are introduced that extend an existing random effect model for multiple outcomes nested in domains.