Robust statistical modeling using the Birnbaum-Saunders-t distribution applied to insurance

Gilberto A. Paula; Víctor Leiva; Michelli Barros; Shuangzhe Liu

doi:10.1002/asmb.887

Robust statistical modeling using the Birnbaum-Saunders-t distribution applied to insurance

Gilberto A. Paula, Víctor Leiva, Michelli Barros, Shuangzhe Liu

Research output: Contribution to journal › Article › peer-review

86 Scopus citations

Abstract

In this paper, we carry out robust modeling and influence diagnostics in Birnbaum-Saunders (BS) regression models. Specifically, we present some aspects related to BS and log-BS distributions and their generalizations from the Student-t distribution, and develop BS-t regression models, including maximum likelihood estimation based on the EM algorithm and diagnostic tools. In addition, we apply the obtained results to real data from insurance, which shows the uses of the proposed model.

Original language	English
Pages (from-to)	16-34
Number of pages	19
Journal	Applied Stochastic Models in Business and Industry
Volume	28
Issue number	1
DOIs	https://doi.org/10.1002/asmb.887
State	Published - Jan 2012
Externally published	Yes

Keywords

EM algorithm
generalized Birnbaum-Saunders and sinh-normal distributions
influence diagnostics
likelihood methods
log-linear models
robustness

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10.1002/asmb.887

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abstract = "In this paper, we carry out robust modeling and influence diagnostics in Birnbaum-Saunders (BS) regression models. Specifically, we present some aspects related to BS and log-BS distributions and their generalizations from the Student-t distribution, and develop BS-t regression models, including maximum likelihood estimation based on the EM algorithm and diagnostic tools. In addition, we apply the obtained results to real data from insurance, which shows the uses of the proposed model.",

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KW - log-linear models

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Robust statistical modeling using the Birnbaum-Saunders-t distribution applied to insurance

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Keywords

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