J
Jean D. Opsomer
Researcher at Colorado State University
Publications - 99
Citations - 3581
Jean D. Opsomer is an academic researcher from Colorado State University. The author has contributed to research in topics: Estimator & Nonparametric regression. The author has an hindex of 29, co-authored 89 publications receiving 3265 citations. Previous affiliations of Jean D. Opsomer include Westat & Iowa State University.
Papers
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Journal ArticleDOI
Nonparametric Regression with Correlated Errors
TL;DR: In this article, the authors review the existing literature in kernel regression, smoothing splines and wavelet regression under correlation, both for short-range and long-range depen- dence.
Journal ArticleDOI
Fitting a Bivariate Additive Model by Local Polynomial Regression
Jean D. Opsomer,David Ruppert +1 more
TL;DR: In this article, the additive model is fitted by local polynomial regression and sufficient conditions guaranteeing the existence of unique estimators for the bivariate additive model are given, and asymptotic approximations to the bias and the variance of a homoscedastic bivariate model with local POlynomial terms of odd and even degree are computed.
Posted Content
Fitting a Bivariate Additive Model by Local Polynomial Regression
Jean D. Opsomer,David Ruppert +1 more
TL;DR: In this paper, the additive model is fitted by local polynomial regression and sufficient conditions guaranteeing the existence of unique estimators for the bivariate additive model are given, and asymptotic approximations to the bias and the variance of a homoscedastic bivariate model with local POlynomial terms of odd and even degree are computed.
Journal ArticleDOI
Asymptotic properties of penalized spline estimators
TL;DR: In this article, the authors study the class of penalized spline estimators, which enjoy similarities to both regression splines, without penalty and with fewer knots than data points, and smoothing splines with knots equal to the data points and a penalty controlling the roughness of the fit.
Journal ArticleDOI
Local polynomial regresssion estimators in survey sampling
F. Jay Breidt,Jean D. Opsomer +1 more
TL;DR: In this paper, a class of estimators based on local polynomial regression is proposed, which are weighted linear combinations of study variables, in which the weights are calibrated to known control totals, but the assumptions on the superpopulation model are considerably weaker.