Estimating smooth distribution function in the presence of heteroscedastic measurement errors

被引：16

作者：

Wang, Xiao-Feng ^{[1
]}

Fan, Zhaozhi ^{[2
]}

Wang, Bin ^{[3
]}

机构：

[1] Cleveland Clin, Dept Quantitat Hlth Sci Biostat, Cleveland, OH 44195 USA

[2] Mem Univ Newfoundland, Dept Math & Stat, St John, NF A1C 5S7, Canada

[3] Univ S Alabama, Dept Math & Stat, Mobile, AL 36688 USA

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2010年 / 54卷 / 01期

关键词：

SIMULATION-EXTRAPOLATION; DENSITY-ESTIMATION; DECONVOLUTION PROBLEMS; BANDWIDTH SELECTION; OPTIMAL RATES; REGRESSION; CONVERGENCE; VARIABLES; QUANTILES;

D O I：

10.1016/j.csda.2009.08.012

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Measurement error occurs in many biomedical fields. The challenges arise when errors are heteroscedastic since we literally have only one observation for each error distribution. This paper concerns the estimation of smooth distribution function when data are contaminated with heteroscedastic errors. We study two types of methods to recover the unknown distribution function: a Fourier-type deconvolution method and a simulation extrapolation (SIMEX) method. The asymptotics of the two estimators are explored and the asymptotic pointwise confidence bands of the SIMEX estimator are obtained. The finite sample performances of the two estimators are evaluated through a simulation study. Finally, we illustrate the methods with medical rehabilitation data from a neuro-muscular electrical stimulation experiment. (C) 2009 Elsevier B.V. All rights reserved.

引用

页码：25 / 36

页数：12

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