Additive Regression Models for Censored Data

Kim, Chul-Ki

Journal of Korean Society for Quality Management > Volume 24(1); 1996 > Article

Journal of Korean Society for Quality Management 1996;24(1): 32-.

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이화여대 통계학과

Additive Regression Models for Censored Data

Chul-Ki Kim

Dept. of Statistics, Ewha Womans University

ABSTRACT

In this paper we develop nonparametric methods for regression analysis when the response variable is subject to censoring that arises naturally in quality engineering. This development is based on a general missing information principle that enables us to apply, via an iterative scheme, nonparametric regression techniques for complete data to iteratively reconstructed data from a given sample with censored observations. In particular, additive regression models are extended to right-censored data. This nonparametric regression method is applied to a simulated data set and the estimated smooth functions provide insights into the relationship between failure time and explanatory variables in the data.

Key Words: Additive regression models;Right-censored data;Missing information principle;Smoothing;