Journal article
Skew-normal antedependence models for skewed longitudinal data
Biometrika, Vol.103(2), pp.363-376
06/01/2016
DOI: 10.1093/biomet/asw006
PMID: 27279663
Abstract
Antedependence models, also known as transition models, have proven to be useful for longitudinal data exhibiting serial correlation, especially when the variances and/or same-lag correlations are time-varying. Statistical inference procedures associated with normal antedependence models are well-developed and have many nice properties, but they are not appropriate for longitudinal data that exhibit considerable skewness. We propose two direct extensions of normal antedependence models to skew-normal antedependence models. The first is obtained by imposing antedependence on a multivariate skew-normal distribution, and the second is a sequential autoregressive model with skew-normal innovations. For both models, necessary and sufficient conditions for pth-order antedependence are established, and likelihood-based estimation and testing procedures for models satisfying those conditions are developed. The procedures are applied to simulated data and to real data from a study of cattle growth.
Details
- Title: Subtitle
- Skew-normal antedependence models for skewed longitudinal data
- Creators
- Shu-Ching Chang - Providence St. Vincent Medical CenterDale L Zimmerman - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Biometrika, Vol.103(2), pp.363-376
- Publisher
- OXFORD UNIV PRESS
- DOI
- 10.1093/biomet/asw006
- PMID
- 27279663
- ISSN
- 0006-3444
- eISSN
- 1464-3510
- Number of pages
- 14
- Language
- English
- Date published
- 06/01/2016
- Academic Unit
- Statistics and Actuarial Science; Biostatistics
- Record Identifier
- 9984257630602771
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