CAJ | 학술논문

为了分析删失数据,该文考虑变系数部分线性模型,此模型允许协变量对响应变量存在非线性影响.响应变量与协变量之间关系的统计模型通过线性结构来拟合是非常重要而且有益.对于删失数据,常用的统计方法不能直接应用于此模型.该文首先提出一类数据变换用以建立无偏条件期望.然后利用profile最小二乘方法,给出了模型中参数分量和非参数分量的profile最小二乘估计,并建立了这些估计的渐近正态性.最后通过数值例子来说明该文所提出的方法的有效性.
위료분석산실수거,해문고필변계수부분선성모형,차모형윤허협변량대향응변량존재비선성영향.향응변량여협변량지간관계적통계모형통과선성결구래의합시비상중요이차유익.대우산실수거,상용적통계방법불능직접응용우차모형.해문수선제출일류수거변환용이건립무편조건기망.연후이용profile최소이승방법,급출료모형중삼수분량화비삼수분량적profile최소이승고계,병건립료저사고계적점근정태성.최후통과수치례자래설명해문소제출적방법적유효성.
This paper considers varying-coefficient partially linear model,which allows one to examine the extent to which covariates interact nonlinearly with an exposure variable,for analysis of censored data. It is very important and useful to fit statistical models by linear construction for response and covariates. For censored data,the usual statistical techniques are not directly applicable to this model. In this paper,we propose a class of transformations of the data so that a unbiased conditional expectation is established. Then the profile least-squares estimators for the parametric and nonparametric components are proposed by profile least-squares technique. The asymptotic normality of the proposed estimators are established. Some numerical examples are given to illustrate the effectiveness of the proposed procedures.