 Robust modeling of multivariate longitudinal data using modified Cholesky and hypersphere decompositionsAnbin Rhee, Min-Sun Kwak and Keunbaik Lee Computational Statistics & Data Analysis, 2022, vol. 170, issue C Abstract: Due to the convenience of the statistical interpretation and parameter estimation, a normal distribution is typically assumed for multivariate longitudinal data analysis. However, this assumption may be questionable in practice, because it is possible that outliers exist or that the underlying data will show heavy tails. In addition, since the covariance matrix should explain complex correlation structures, it must be positive-definite, and as it is also high-dimensional, the modeling of the covariance matrix is not easy. To solve these problems, we propose the robust modeling of multivariate longitudinal data by considering multivariate t distribution, and we exploit modified Cholesky and hypersphere decompositions to model the covariance matrix. The estimation of the models is shown to be robust when the data include outliers and exhibit heavy tails. The performance of our proposed model and algorithm is illustrated using a nonalcoholic fatty liver disease data set and several simulation studies. Keywords: Autoregressive; Correlation matrix; Heterogeneity; Innovation variance; t distribution; Positive definite (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:170:y:2022:i:c:s0167947322000196 DOI: 10.1016/j.csda.2022.107439 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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