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Survival Analysis Using a Censored Semiparametric Regression Model

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Author Info
Jesus Orbe () (Departamento de Econometria y Estadistica (Economia Aplicada III), Universidad del Pais Vasco, Spain)
Eva Ferreira () (Departamento de Econometria y Estadistica (Economia Aplicada III), Universidad del Pais Vasco, Spain)
Vicente Nunez-Anton () (Departamento de Econometria y Estadistica (Economia Aplicada III), Universidad del Pais Vasco, Spain)

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Abstract

In this work we study the effect of several covariates X on a censored response variable T with unknown probability distribution. A semiparametric model is proposed to consider situations where the functional form of the effect of one or more covariates is unknown. We provide its estimation procedure and, in addition, a bootstrap technique to make inference on the parameters. An application with a real dataset is presented, as well as some simulation results, to demonstrate the good behavior of the proposed estimation process and to analyze the effect of the censorship. This new model has an important application field in reliability, survival or lifetime data analysis.

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File URL: http://www.et.bs.ehu.es/biltoki/EPS/dt200007.pdf
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Publisher Info
Paper provided by Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística) in its series BILTOKI with number 200007.

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Date of creation: 25 Apr 2000
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Handle: RePEc:ehu:biltok:200007

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Postal: Dpto. de Econometría y Estadística, Facultad de CC. Económicas y Empresariales, Universidad del País Vasco, Avda. Lehendakari Aguirre 83, 48015 Bilbao, Spain
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Related research
Keywords: Lifetime Data Models Censorship Kaplan-Meier Bootstrap Nonparametric Estimation.

Find related papers by JEL classification:
C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis
C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods

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