SEMIPARAMETRIC REGRESSION IN TESTICULAR GERM CELL DATA

• 成果类型: Article
• 署名作者: Voulgaraki, Anastasia; Kedem, Benjamin; Graubard, Barry I.
• 署名单位: University System of Maryland; University of Maryland College Park; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI)
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/12-AOAS552
• 发表日期: 2012
• 页码: 1185-1208
• 关键词: GOODNESS-OF-FIT; DENSITY-ESTIMATION; models; INFORMATION; likelihood; RISK

摘要

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.