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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.)

Likelihood-based inference for spatiotemporal data with censored and missing responses

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Author(s):
Valeriano, Katherine A. L. [1] ; Lachos, Victor H. [2] ; Prates, Marcos O. [3] ; Matos, Larissa A. [1]
Total Authors: 4
Affiliation:
[1] Univ Estadual Campinas, Dept Stat, Sao Paulo, SP - Brazil
[2] Univ Connecticut, Dept Stat, Storrs, CT 06269 - USA
[3] Univ Fed Minas Gerais, Dept Stat, Belo Horizonte, MG - Brazil
Total Affiliations: 3
Document type: Journal article
Source: ENVIRONMETRICS; v. 32, n. 3 DEC 2020.
Web of Science Citations: 0
Abstract

This paper proposes an alternative method to deal with spatiotemporal data with censored and missing responses using the SAEM algorithm. This algorithm is a stochastic approximation of the widely used EM algorithm and is an important tool for models in which the E-step does not have an analytic form. Besides the algorithm developed to estimate the model parameters from a likelihood-based perspective, we present analytical expressions to compute the observed information matrix. Global influence measures are also developed and presented. Several simulation studies are conducted to examine the asymptotic properties of the SAEM estimates. The proposed method is illustrated by environmental data analysis. The computing codes are implemented in the new R package StempCens. (AU)

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