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

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008  230613e20231205usa|||p |0|||b|eng d
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎6
1001 ‎$0‎MAPA20190015158‎$a‎Chai Fung , Tsz
24510‎$a‎Fitting censored and truncated regression data using the mixture of experts models‎$c‎Tsz Chai Fung, Andrei L. Badescu & X. Sheldon Lin
520  ‎$a‎The 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
650 4‎$0‎MAPA20080579258‎$a‎Cálculo actuarial
650 4‎$0‎MAPA20080593063‎$a‎Regresión no lineal
650 4‎$0‎MAPA20080568085‎$a‎Bases de datos
650 4‎$0‎MAPA20080578848‎$a‎Análisis de datos
650 4‎$0‎MAPA20080553128‎$a‎Algoritmos
7001 ‎$0‎MAPA20210030147‎$a‎Badescu, Andrei L.
7001 ‎$0‎MAPA20170014539‎$a‎Sheldon Lin, X.
7730 ‎$w‎MAP20077000239‎$g‎05/12/2022 Tomo 26 Número 4 - 2022 , p. 496-520‎$x‎1092-0277‎$t‎North American actuarial journal‎$d‎Schaumburg : Society of Actuaries, 1997-
85600‎$y‎MÁS INFORMACIÓN‎$u‎ mailto:centrodocumentacion@fundacionmapfre.org?subject=Consulta%20de%20una%20publicaci%C3%B3n%20&body=Necesito%20m%C3%A1s%20informaci%C3%B3n%20sobre%20este%20documento%3A%20%0A%0A%5Banote%20aqu%C3%AD%20el%20titulo%20completo%20del%20documento%20del%20que%20desea%20informaci%C3%B3n%20y%20nos%20pondremos%20en%20contacto%20con%20usted%5D%20%0A%0AGracias%20%0A