Robust functional regression model for marginal mean and subject-specific inferences. (November 2018)

• Record Type: Journal Article
• Authors: Cao, Chunzheng; Shi, Jian Qing; Lee, Youngjo
• Is Part Of: Statistical methods in medical research. Volume 27:Number 11(2018)
• Page Start: 3236
• Page End: 3254
• Publication Date: 2018-11
• Subjects: Bootstrap -- dose–response -- EM algorithm -- functional data analysis -- heavy-tailed process -- outliers -- prediction interval -- robust; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280217695346
• Languages: English
• ISSNs: 0962-2802

Abstract

We introduce flexible robust functional regression models, using various heavy-tailed processes, including a Student t -process. We propose efficient algorithms in estimating parameters for the marginal mean inferences and in predicting conditional means as well as interpolation and extrapolation for the subject-specific inferences. We develop bootstrap prediction intervals (PIs) for conditional mean curves. Numerical studies show that the proposed model provides a robust approach against data contamination or distribution misspecification, and the proposed PIs maintain the nominal confidence levels. A real data application is presented as an illustrative example.