For censored and truncated data |
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Authors: | Chul-Ki Kim Tze Leung Lai |
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Institution: | Department of Statistics , Ewha Womans Universitv , Seodaemungu, Seoul, 120-750, Korea |
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Abstract: | In this paper we develop nonparametric methods for regression analysis when the response variable is subject to censoring and/or truncation. The development is based on a data completion princple that enables us to apply, via an iterative scheme, nonparametric regression techniques to iteratively com¬pleted data from a given sample with censored and/or truncated observations. In particular, locally weighted regression smoothers and additive regression models are extended to left-truncated and right-censored data Nonparamet¬ric regression analysis is applied to the Stanford heart transplant data, which have been analyzed by previous authors using semiparametric regression meth¬ods. and provides new insights into the relationship between expected survival time after a heart transplant and explanatory variables. |
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Keywords: | additive regression models data completion principle Left tranucated and right censored data Locally weiyhlcd ityrthsion Srnooiirmu |
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