ARMA Cholesky factor models for the covariance matrix of linear models

Keunbaik Lee, Changryong Baek and Michael J. Daniels

Computational Statistics & Data Analysis, 2017, vol. 115, issue C, 267-280

Abstract: In longitudinal studies, serial dependence of repeated outcomes must be taken into account to make correct inferences on covariate effects. As such, care must be taken in modeling the covariance matrix. However, estimation of the covariance matrix is challenging because there are many parameters in the matrix and the estimated covariance matrix should be positive definite. To overcome these limitations, two Cholesky decomposition approaches have been proposed: modified Cholesky decomposition for autoregressive (AR) structure and moving average Cholesky decomposition for moving average (MA) structure, respectively. However, the correlations of repeated outcomes are often not captured parsimoniously using either approach separately. In this paper, we propose a class of flexible, nonstationary, heteroscedastic models that exploits the structure allowed by combining the AR and MA modeling of the covariance matrix that we denote as ARMACD. We analyze a recent lung cancer study to illustrate the power of our proposed methods.

Keywords: Cholesky decomposition; Longitudinal data; Heteroscedastic (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:115:y:2017:i:c:p:267-280

DOI: 10.1016/j.csda.2017.05.001

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