Robust functional regression model for marginal mean and subject-specific inferences

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...

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Bibliographic Details
Published inStatistical methods in medical research Vol. 27; no. 11; p. 3236
Main Authors Cao, Chunzheng, Shi, Jian Qing, Lee, Youngjo
Format Journal Article
LanguageEnglish
Published England 01.11.2018
Subjects
Algorithms
Biomedical Research - statistics & numerical data
Normal Distribution
Regression Analysis
dose–response
prediction interval
robust
heavy-tailed process
Bootstrap
functional data analysis
outliers
EM algorithm
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Summary: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.
ISSN:1477-0334
DOI:10.1177/0962280217695346