Kernel estimation for a superpopulation probability density function under informative selection

被引：2
作者：
Bonnéry D. [1 ]
Breidt F.J. [2 ]
Coquet F. [3 ]
机构：
[1] Joint Program in Survey Methodology, 1218 LeFrak Hall, University of Maryland, College Park, 20742, MD
[2] Department of Statistics, Colorado State University, Fort Collins, 80523-1877, CO
[3] Irmar and Ensai, Campus de Ker-Lann, BP 37203, Rue Blaise Pascal, Bruz-Cedex
来源：
METRON | 2017年 / 75卷 / 3期
关键词：
Bochner's lemma; Complex survey; Nadaraya-Watson estimator; Survey weighting D;
D O I：
10.1007/s40300-017-0127-x
中图分类号：
学科分类号：
摘要：
Kernel density estimation of the probability density function (pdf) of a response variable is considered under informative selection from a finite population. The informative selection implies that the conditional pdf of a response, given that it was selected for observation, is not the same as the inferential target, which is the unconditional pdf of the response in the superpopulation. Instead, the pdf of the observations (sample pdf) is a weighted version of the superpopulation pdf of interest. Properties of the standard kernel density estimator are described under an asymptotic framework that covers a wide range of informative selection mechanisms. The theory allows for the possibility that the selection mechanism has a parametric structure. A variety of adjustments (parametric or nonparametric) to account for the informative selection are proposed, and investigated via simulation. © 2017 Sapienza Università di Roma.
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收藏
页码：301 / 318
页数：17
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