ECM-based maximum likelihood inference for multivariate linear mixed models with autoregressive errors

For the analysis of longitudinal data with multiple characteristics, we are devoted to providing additional tools for multivariate linear mixed models in which the errors are assumed to be serially correlated according to an autoregressive process. We present a computationally flexible ECM procedure...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Computational statistics & data analysis Ročník 54; číslo 5; s. 1328 - 1341
Hlavní autoři: Wang, Wan-Lun, Fan, Tsai-Hung
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.05.2010
Elsevier
Edice:Computational Statistics & Data Analysis
Témata:
Exact sciences and technology
General topics
Linear inference, regression
Mathematics
Multivariate analysis
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in probability and statistics
Probability and statistics
Sciences and techniques of general use
Statistics
Score test
Error estimation
Prediction theory
Autoregressive model
Multivariate analysis
Stochastic process
Statistical simulation
Linear model
Statistical test
Repeated measurement
Random effect
Score statistics
Data analysis
Test statistic
Prediction
AIDS
Statistical association
Statistical estimation
Infection
Statistical computation
Correlation analysis
Viral disease
Mixed model
Filtering theory
Maximum likelihood
Autocorrelation
Autoregressive processes
ISSN:0167-9473, 1872-7352
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:For the analysis of longitudinal data with multiple characteristics, we are devoted to providing additional tools for multivariate linear mixed models in which the errors are assumed to be serially correlated according to an autoregressive process. We present a computationally flexible ECM procedure for obtaining the maximum likelihood estimates of model parameters. A score test statistic for testing the existence of autocorrelation among within-subject errors of each characteristic is derived. The techniques for the estimation of random effects and the prediction of further responses given past repeated measures are also investigated. The methodology is illustrated through an application to a set of AIDS data and two small simulation studies.
ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2009.11.021