×

zbMATH — the first resource for mathematics

Joint mean-covariance models with applications to longitudinal data: Unconstrained parameterisation. (English) Zbl 0949.62066
Summary: We provide unconstrained parameterisation for and model a covariance using covariates. the Cholesky decomposition of the inverse of a covariance matrix is used to associate a unique unit lower triangular and a unique diagonal matrix with each covariance matrix. The entries of the lower triangular and the log of the diagonal matrix are unconstrained and have meaning as regression coefficients and prediction variances when regressing a measurement on its predecessors.
An extended generalised linear model is introduced for joint modelling of the vectors of predictors for the mean and covariance subsuming the joint modelling strategy for mean and variance heterogeneity, K.R. Gabriel’s [Ann. Math. Stat. 33, 201-212 (1962; Zbl 0111.15604)] antependence models, A.P. Dempster’s [Biometrika 28, 157-175 (1972)] covariance selection models and the class of graphical models. The likelihood function and maximum likelihood estimators of the covariance and the mean parameters are studied when the observations are normally distributed. Applications to modelling nonstationary dependence structures and multivariate data are discussed and illustrated using real data. A graphical method, similar to that based on the correlogram in time series, is developed and used to identify parametric models for nonstationary covariances.

MSC:
62J12 Generalized linear models (logistic models)
62H12 Estimation in multivariate analysis
PDF BibTeX XML Cite
Full Text: DOI