Semiparametric Estimation for Two-Sample Location-Scale Models under Type I Censorship |
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Authors: | Jun-Qiang Yang Xuewen Lu Radhey S. Singh |
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Affiliation: | 1. Hunan Urban Construction College , Xiangtan, Hunan, China xpyjq1668@163.com;3. Department of Mathematics and Statistics , University of Calgary , Calgary, Alberta, Canada;4. Department of Mathematics and Statistics , University of Guelph , Guelph, Ontario, Canada |
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Abstract: | To compare two samples under Type I censorship, this article proposes a method of semiparametric inference for the two-sample location-scale problem when the model for two samples is characterized by an unknown distribution and two unknown parameters. Simultaneous estimators for both the location shift and scale change parameters are given. It is shown that the two estimators are strongly consistent and asymptotically normal. The approach in this article can also be used for scale-shape models. Monte Carlo studies indicate that the proposed estimation procedure performs well in finite and heavily censored samples, maintains high relative efficiencies for a wide range of censoring proportions and is robust to the model misspecification, and also outperforms other competitive estimators. |
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Keywords: | Asymptotic normality Location-scale model Semiparametric model Strong consistency Type I censorship |
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