Abstract
Medical and public health research increasingly involves the collection of complex and high dimensional data. In particular, functional data-where the unit of observation is a curve or set of curves that are finely sampled over a grid-is frequently obtained. Moreover, researchers often sample multiple curves per person resulting in repeated functional measures. A common question is how to analyze the relationship between two functional variables. We propose a general function-on-function regression model for repeatedly sampled functional data on a fine grid, presenting a simple model as well as a more extensive mixed model framework, and introducing various functional Bayesian inferential procedures that account for multiple testing. We examine these models via simulation and a data analysis with data from a study that used event-related potentials to examine how the brain processes various types of images.
Original language | English (US) |
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Pages (from-to) | 563-574 |
Number of pages | 12 |
Journal | Biometrics |
Volume | 71 |
Issue number | 3 |
DOIs | |
State | Published - Sep 1 2015 |
Keywords
- Basis functions
- Bayesian inference
- Function-on-function regression
- Functional data analysis
- Functional mixed models
- Functional testing
- Principal components
- Wavelet regression
ASJC Scopus subject areas
- Statistics and Probability
- General Biochemistry, Genetics and Molecular Biology
- General Immunology and Microbiology
- General Agricultural and Biological Sciences
- Applied Mathematics
MD Anderson CCSG core facilities
- Biostatistics Resource Group