Fitting censored and truncated regression data using the mixture of experts models

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<title>Fitting censored and truncated regression data using the mixture of experts models</title>
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<name type="personal" usage="primary" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20190015158">
<namePart>Chai Fung , Tsz</namePart>
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<namePart>Badescu, Andrei L.</namePart>
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<namePart>Sheldon Lin, X.</namePart>
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<abstract displayLabel="Summary">The logit-weighted reduced mixture of experts model (LRMoE) is a flexible yet analytically tractable non-linear regression model. Though it has shown usefulness in modeling insurance loss frequencies and severities, model calibration becomes challenging when censored and truncated data are involved, which is common in actuarial practice. In this article, we present an extended expectationconditional maximization (ECM) algorithm that efficiently fits the LRMoE to random censored and random truncated regression data. The effectiveness of the proposed algorithm is empirically examined through a simulation study. Using real automobile insurance data sets, the usefulness and importance of the proposed algorithm are demonstrated through two actuarial applications: individual claim reserving and deductible ratemaking</abstract>
<note type="statement of responsibility">Tsz Chai Fung, Andrei L. Badescu & X. Sheldon Lin</note>
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<topic>Cálculo actuarial</topic>
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<topic>Regresión no lineal</topic>
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<topic>Bases de datos</topic>
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<topic>Análisis de datos</topic>
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<title>North American actuarial journal</title>
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<publisher>Schaumburg : Society of Actuaries, 1997-</publisher>
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<identifier type="issn">1092-0277</identifier>
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<text>05/12/2022 Tomo 26 Número 4 - 2022 , p. 496-520</text>
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