標題: Extended Gauss-Markov theorem for nonparametric mixed-effects models
作者: Huang, SY
Lu, HHS
交大名義發表
National Chiao Tung University
關鍵字: nonparametric mixed-effects;Gauss-Markov theorem;best linear unbiased prediction (BLUP);regularization;minimaxity;normal equations;nonparametric regression;wavelet shrinkage;deconvolution
公開日期: 1-Feb-2001
摘要: The Gauss-Markov theorem provides a golden standard for constructing the best linear unbiased estimation for linear models. The main purpose of this article is to extend the Gauss-Markov theorem to include nonparametric mixed-effects models. The extended Gauss-Markov estimation (or prediction) is shown to be equivalent to a regularization method and its minimaxity is addressed. The resulting Gauss-Markov estimation serves as an oracle to guide the exploration for effective nonlinear estimators adaptively. Various examples are discussed. Particularly, the wavelet nonparametric regression example and its connection with a Sobolev regularization is presented. (C) 2001 Academic Press.
URI: http://dx.doi.org/10.1006/jmva.2000.1930
http://hdl.handle.net/11536/29859
ISSN: 0047-259X
DOI: 10.1006/jmva.2000.1930
期刊: JOURNAL OF MULTIVARIATE ANALYSIS
Volume: 76
Issue: 2
起始頁: 249
結束頁: 266
Appears in Collections:Articles


Files in This Item:

  1. 000167083100005.pdf