[ABE-L] Divulgação de palestra do Programa de Pós-graduação em Estatística da UnB

[cibele queiroz da silva]

Ciclo de Palestras do Programa de Pós-graduação em Estatística da UnB

 

Divulgação de palestra do Programa de Pós-graduação em Estatística da UnB.

 

Título: Likelihood-based Inference for Mixed-Effects Models with Censored
Response Using the Multivariate-t Distribution.

 

Palestrante: Prof. Victor Hugo Lachos (IME-UNICAMP)

 

DATA: 20/08/2015 (quinta-feira)

HORÁRIO: 14:30h

LOCAL: Sala Multiuso EST

 

Resumo:

 

Mixed-effects models are commonly used to fit longitudinal or repeated
measures data. A complication arises when the response is censored, for
example, due to limits of quantification of the assay used. Although the
normal distributions are commonly assumed for the random effects and the
residual errors, such assumptions make inferences vulnerable in the
presence of outliers. The sensitivity to outliers and the need of heavy
tailed distributions for random effects and residual errors motivate us to
develop a likelihood-based inference for linear and nonlinear mixed effects
models with censored response (NLMEC/LMEC) based on the multivariate
Student-t distribution. An ECM algorithm is developed for computing the
maximum likelihood estimates for NLMEC/LMEC with the standard errors of the
fixed effects and the likelihood function as a by-product. The algorithm
uses the closed-form expressions at the E-step, which relies on the
formulas of the mean and variance of a truncated multivariate-t
distribution. The proposed algorithm is implemented in the R package tlmec.
The proposed methodology is applied to analyze longitudinal HIV viral load
data in the two recent AIDS studies. In addition, a simulation study is
conducted to examine the performance of the proposed method and carry out
the comparison with the approach by Vaida and Liu (2009).

(Joint work with Ming-Hiu Chen, Marcos Oliveira Prates and Larissa Avila
Matos)

 

 
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