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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.)

Multivariate measurement error models based on Student-t distribution under censored responses

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Author(s):
Matos, Larissa A. [1] ; Castro, Luis M. [2] ; Cabral, Celso R. B. [3] ; Lachos, Victor H. [4]
Total Authors: 4
Affiliation:
[1] Univ Estadual Campinas, IMECC, Dept Stat, BR-13083859 Campinas, SP - Brazil
[2] Pontificia Univ Catolica Chile, Dept Stat, Santiago - Chile
[3] Univ Fed Amazonas, Dept Stat, Manaus, Amazonas - Brazil
[4] Univ Connecticut, Dept Stat, Storrs, CT 06269 - USA
Total Affiliations: 4
Document type: Journal article
Source: STATISTICS; v. 52, n. 6, p. 1395-1416, 2018.
Web of Science Citations: 0
Abstract

Measurement error models constitute a wide class of models that include linear and nonlinear regression models. They are very useful to model many real-life phenomena, particularly in the medical and biological areas. The great advantage of these models is that, in some sense, they can be represented as mixed effects models, allowing us to implement wellknown techniques, like the EM-algorithm for the parameter estimation. In this paper, we consider a class of multivariate measurement error models where the observed response and/or covariate are not fully observed, i.e., the observations are subject to certain threshold values below or above which the measurements are not quantifiable. Consequently, these observations are considered censored. We assume a Student-t distribution for the unobserved true values of the mismeasured covariate and the error term of the model, providing a robust alternative for parameter estimation. Our approach relies on a likelihood-based inference using an EM-type algorithm. The proposed method is illustrated through some simulation studies and the analysis of an AIDS clinical trial dataset. (AU)

FAPESP's process: 15/20922-5 - Flexible regression modeling for censored data
Grantee:Víctor Hugo Lachos Dávila
Support type: Research Grants - Visiting Researcher Grant - Brazil
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: 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: 11/22063-9 - APPLICATIONS OF THE SCALE MIXTURES OF SKEW-NORMAL DISTRIBUTIONS IN FACTOR ANALYSIS MODELS
Grantee:Larissa Avila Matos
Support type: Scholarships in Brazil - Doctorate