Open Access
September 2012 Semiparametric regression in testicular germ cell data
Anastasia Voulgaraki, Benjamin Kedem, Barry I. Graubard
Ann. Appl. Stat. 6(3): 1185-1208 (September 2012). DOI: 10.1214/12-AOAS552


It is possible to approach regression analysis with random covariates from a semiparametric perspective where information is combined from multiple multivariate sources. The approach assumes a semiparametric density ratio model where multivariate distributions are “regressed” on a reference distribution. A kernel density estimator can be constructed from many data sources in conjunction with the semiparametric model. The estimator is shown to be more efficient than the traditional single-sample kernel density estimator, and its optimal bandwidth is discussed in some detail. Each multivariate distribution and the corresponding conditional expectation (regression) of interest are estimated from the combined data using all sources. Graphical and quantitative diagnostic tools are suggested to assess model validity. The method is applied in quantifying the effect of height and age on weight of germ cell testicular cancer patients. Comparisons are made with multiple regression, generalized additive models (GAM) and nonparametric kernel regression.


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Anastasia Voulgaraki. Benjamin Kedem. Barry I. Graubard. "Semiparametric regression in testicular germ cell data." Ann. Appl. Stat. 6 (3) 1185 - 1208, September 2012.


Published: September 2012
First available in Project Euclid: 31 August 2012

zbMATH: 1254.62056
MathSciNet: MR3012526
Digital Object Identifier: 10.1214/12-AOAS552

Keywords: diagnostic , GAM , ‎kernel‎ , Multivariate density ratio model , Nadaraya–Watson , random covariates

Rights: Copyright © 2012 Institute of Mathematical Statistics

Vol.6 • No. 3 • September 2012
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