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

Gilberto Paula; Victor Leiva; Michelli Barros; Shuangzhe Liu

doi:10.1002/asmb.887

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

Gilberto Paula, Victor Leiva, Michelli Barros, Shuangzhe Liu

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

93 Citations (Scopus)

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
Publication status	Published - 2012

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title = "Robust statistical modeling using the Birnbaum-Saunders-t distribution applied to insurance",

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",

author = "Gilberto Paula and Victor Leiva and Michelli Barros and Shuangzhe Liu",

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AB - 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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JO - Applied Stochastic Models in Business and Industry

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

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