JOURNAL ARTICLE

Nonparametric regression with cross-classified responses

Chong GuPing Ma

Year: 2011 Journal:   Canadian Journal of Statistics Vol: 39 (4)Pages: 591-609   Publisher: Wiley

Abstract

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, Kullback–Leibler 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: 591–609; 2011. © 2011 Statistical Society of Canada Dans cet article, nous developpons des modeles de regression avec variables reponses provenant de classifications croisees. Les structures d'independance conditionnelle peuvent etre explorees/exploitees grâce a l'inclusion/exclusion de termes dans une decomposition de type anova fonctionnelle et l'estimation se fait de facon non parametrique en utilisant la methode de vraisemblance penalisee. Un ensemble d'outils informatiques et d'analyse de donnees sont presentes et incluent la validation croisee pour la selection du parametre de lissage, la projection de Kullback-Leibler pour la selection de modeles et les intervalles de credibilite bayesiens pour les rapports de cotes. Des effets aleatoires sont inclus dans le modele pour prendre en compte les possibles correlations telles que celles trouvees dans les donnees longitudinales et en grappes. Des etudes de simulations limitees montrent les performances empiriques de ces methodes. Un vrai jeu de donnees sur l'oculometrie dans des etudes linguistiques est aussi traite. Ces techniques sont implantees dans un ensemble de fonctions R et nous en presentons brievement l'utilisation en annexe. La revue canadienne de statistique 39: 591–609; 2011. © 2011 Societe statistique du Canada

Keywords:
Statistics Mathematics Confidence interval Econometrics

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Topics

Bayesian Methods and Mixture Models
Physical Sciences →  Computer Science →  Artificial Intelligence
Sensory Analysis and Statistical Methods
Life Sciences →  Agricultural and Biological Sciences →  Food Science

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