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Regression smoothers and additive models for censored and truncated data

Title
Regression smoothers and additive models for censored and truncated data
Authors
Kim, CKLai, TL
Ewha Authors
김철기
Issue Date
1999
Journal Title
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
ISSN
0361-0926JCR Link
Citation
vol. 28, no. 11, pp. 2717 - 2747
Keywords
additive regression modelsdata completion principleleft-truncated and right-censored datalocally weighted regressionsmoothing
Publisher
MARCEL DEKKER INC
Indexed
SCIE; SCOPUS WOS
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 principle that enables us to apply, via an iterative scheme, nonparametric regression techniques to iteratively completed 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. Nonparametric regression analysis is applied to the Stanford heart transplant data, which have been analyzed by previous authors using semiparametric regression methods, and provides new insights into the relationship between expected survival time after a heart transplant and explanatory variables.
DOI
10.1080/03610929908832447
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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