View : 25 Download: 0
Regression smoothers and additive models for censored and truncated data
- Regression smoothers and additive models for censored and truncated data
- Kim, CK; Lai, TL
- Ewha Authors
- Issue Date
- Journal Title
- COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
- vol. 28, no. 11, pp. 2717 - 2747
- additive regression models; data completion principle; left-truncated and right-censored data; locally weighted regression; smoothing
- MARCEL DEKKER INC
- SCIE; SCOPUS
- 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.
- Appears in Collections:
- 자연과학대학 > 통계학전공 > Journal papers
- Files in This Item:
There are no files associated with this item.
- RIS (EndNote)
- XLS (Excel)
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.