Conditional density estimation in measurement error problems

JOURNAL OF MULTIVARIATE ANALYSIS

卷:133

Article

Wang, Xiao-Feng¹  Ye, Deping² 

[1] Cleveland Clin, Dept Quantitat Hlth Sci, Lerner Res Inst, Biostat Sect, Cleveland, OH 44195 USA

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

关键词: Measurement error;    Gene microarray;    Conditional density;    Deconvolution;    Ridge parameter;    Kernel;    Bandwidth selection;   

DOI  :  10.1016/j.jmva.2014.08.011

来源: Elsevier

【 摘 要 】

This paper is motivated by a wide range of background correction problems in gene array data analysis, where the raw gene expression intensities are measured with error. Estimating a conditional density function from the contaminated expression data is a key aspect of statistical inference and visualization in these studies. We propose re-weighted deconvolution kernel methods to estimate the conditional density function in an additive error model, when the error distribution is known as well as when it is unknown. Theoretical properties of the proposed estimators are investigated with respect to the mean absolute error from a double asymptotic view. Practical rules are developed for the selection of smoothing-parameters. Simulated examples and an application to an Illumina bead microarray study are presented to illustrate the viability of the methods. (C) 2014 Elsevier Inc. All rights reserved.

【 授权许可】

Free