Premium
State‐space models for multivariate longitudinal data of mixed types
Author(s) -
Jørgensen Bent,
LundbyeChristensen Søren,
Song Peter XueKun,
Sun Li
Publication year - 1996
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3315747
Subject(s) - covariate , multivariate statistics , mathematics , latent variable , latent class model , statistics , parametric statistics , state space , econometrics , latent variable model , exponential family , computer science
We propose a class of state‐space models for multivariate longitudinal data where the components of the response vector may have different distributions. The approach is based on the class of Tweedie exponential dispersion models, which accommodates a wide variety of discrete, continuous and mixed data. The latent process is assumed to be a Markov process, and the observations are conditionally independent given the latent process, over time as well as over the components of the response vector. This provides a fully parametric alternative to the quasilikelihood approach of Liang and Zeger. We estimate the regression parameters for time‐varying covariates entering either via the observation model or via the latent process, based on an estimating equation derived from the Kalman smoother. We also consider analysis of residuals from both the observation model and the latent process.