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Bayesian analysis of longitudinal and multidimensional functional data
- Source :
- Biostatistics, Biostatistics (Oxford, England), vol 23, iss 2
- Publication Year :
- 2019
-
Abstract
- Summary Multi-dimensional functional data arises in numerous modern scientific experimental and observational studies. In this article, we focus on longitudinal functional data, a structured form of multidimensional functional data. Operating within a longitudinal functional framework we aim to capture low dimensional interpretable features. We propose a computationally efficient nonparametric Bayesian method to simultaneously smooth observed data, estimate conditional functional means and functional covariance surfaces. Statistical inference is based on Monte Carlo samples from the posterior measure through adaptive blocked Gibbs sampling. Several operative characteristics associated with the proposed modeling framework are assessed comparatively in a simulated environment. We illustrate the application of our work in two case studies. The first case study involves age-specific fertility collected over time for various countries. The second case study is an implicit learning experiment in children with autism spectrum disorder.
- Subjects :
- Statistics and Probability
Computer science
Autism Spectrum Disorder
Intellectual and Developmental Disabilities (IDD)
Autism
Statistics & Probability
Monte Carlo method
Bayesian probability
Tensor spline
computer.software_genre
01 natural sciences
Measure (mathematics)
010104 statistics & probability
03 medical and health sciences
symbols.namesake
Statistical inference
Genetics
Rank regularization
Humans
0101 mathematics
Gaussian process
Child
030304 developmental biology
0303 health sciences
Statistics
Functional data analysis
Bayes Theorem
General Medicine
Articles
Covariance
Brain Disorders
Marginal covariance
Longitudinal mixed model
Mental Health
symbols
Data mining
Statistics, Probability and Uncertainty
Factor analysis
computer
Monte Carlo Method
Gibbs sampling
Subjects
Details
- ISSN :
- 14684357
- Volume :
- 23
- Issue :
- 2
- Database :
- OpenAIRE
- Journal :
- Biostatistics (Oxford, England)
- Accession number :
- edsair.doi.dedup.....91a9bcc40972caea4c66169c8cbff8dc