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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Heavy-tailed longitudinal regression models for censored data: a robust parametric approach

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Matos, Larissa A. [1] ; Lachos, Victor H. [2] ; Lin, Tsung-I [3, 4] ; Castro, Luis M. [5]
Total Authors: 4
[1] Univ Estadual Campinas, Dept Stat, Campinas, SP - Brazil
[2] Univ Connecticut, Dept Stat, Storrs, CT 06269 - USA
[3] Natl Chung Hsing Univ, Inst Stat, Taichung 402 - Taiwan
[4] China Med Univ, Dept Publ Hlth, Taichung 404 - Taiwan
[5] Pontificia Univ Catolica Chile, Dept Stat, Santiago - Chile
Total Affiliations: 5
Document type: Journal article
Source: TEST; v. 28, n. 3, p. 844-878, SEP 2019.
Web of Science Citations: 0

Longitudinal HIV-1 RNA viral load measures are often subject to censoring due to upper and lower detection limits depending on the quantification assays. A complication arises when these continuous measures present a heavy-tailed behavior because inference can be seriously affected by the misspecification of their parametric distribution. For such data structures, we propose a robust nonlinear censored regression model based on the scale mixtures of normal distributions. By taking into account the autocorrelation existing among irregularly observed measures, a damped exponential correlation structure is considered. A stochastic approximation of the EM algorithm is developed to obtain the maximum likelihood estimates of the model parameters. The main advantage of this new procedure os to allow estimating the parameters of interest and evaluating the log-likelihood function easily and quickly. Furthermore, the standard errors of the fixed effects and predictions of unobservable values of the response can be obtained as a byproduct. The practical utility of the proposed method is exemplified using both simulated and real data. (AU)

FAPESP's process: 15/05385-3 - Estimation in mixed-effects models with censored response using scale mixtures of normal distributions
Grantee:Larissa Avila Matos
Support type: Scholarships abroad - Research Internship - Doctorate
FAPESP's process: 18/05013-7 - Semiparametric mixed effects models with multiple censored response using scale mixtures of normal distributions
Grantee:Larissa Avila Matos
Support type: Research Grants - Visiting Researcher Grant - International
FAPESP's process: 14/02938-9 - Estimation and diagnostics for censored mixed effects models using scale mixtures of skew-normal distributions
Grantee:Víctor Hugo Lachos Dávila
Support type: Regular Research Grants
Grantee:Larissa Avila Matos
Support type: Scholarships in Brazil - Doctorate