Censored Regression: Local Linear Approximations and Their Applications
提出一种基于删失数据的局部加权最小二乘回归方法，无需假设回归形式，通过简单变换数据并自适应调整带宽，适用于医学等领域的删失数据分析。
Various statistical tools are available for modeling the relationship between response and covariate if the data are fully observable. In the situation of censored data, however, those tools are no longer directly applicable. This article provides an easily implemented methodology for modeling the association, based on censored data. The form of the regression relationship will be completely determined by the data; no assumptions are made about this form. Basic ideas behind the methodology are to transform the observed data in an appropriate simple way and then to apply a locally weighted least squares regression. The proposed estimator involves a variable bandwidth that automatically adapts to the design of the data points. That the methodology is very easy to implement is illustrated by several examples, including simulation studies and an analysis of the Stanford Heart Transplant Data and the Primary Biliary Cirrhosis Data. Several theoretical considerations are reflected in the examples. Finally, some basic asymptotic results are established.