Nonparametric regression with cross-classified responses

作者：Gu Chong^*; Ma Ping

来源：Canadian Journal of Statistics, 2011, 39(4): 591-609.

DOI：10.1002/cjs.10125

• Conditional density
• cross-validation
• eyetracking experiments
• penalized likelihood
• random effects
• MSC 2010
• Primary 62G08
• secondary 62G05
• 62G20
• 62H12
• 41A15

In this article, we develop regression models with cross-classified responses. Conditional independence structures can be explored/exploited through the selective inclusion/exclusion of terms in a certain functional ANOVA decomposition, and the estimation is done nonparametrically via the penalized likelihood method. A cohort of computational and data analytical tools are presented, which include cross-validation for smoothing parameter selection, KullbackLeibler projection for model selection, and Bayesian confidence intervals for odds ratios. Random effects are introduced to model possible correlations such as those found in longitudinal and clustered data. Empirical performances of the methods are explored in simulation studies of limited scales, and a real data example is presented using some eyetracking data from linguistic studies. The techniques are implemented in a suite of R functions, whose usage is briefly described in the appendix. The Canadian Journal of Statistics 39: 591609; 2011.

• 出版日期2011-12