Nonparametric lack-of-fit tests for parametric mean-regression models with censored data

被引：14

作者：

Lopez, O. ^{[1
]}

Patilea, V. ^{[1
]}

机构：

[1] CREST ENSAI, F-35172 Bruz, France

来源：

JOURNAL OF MULTIVARIATE ANALYSIS | 2009年 / 100卷 / 01期

关键词：

Hypothesis testing; Censored data; Kaplan-Meier integral; Local alternative; SYNTHETIC DATA; LINEAR-MODELS; SURVIVAL-DATA; U-PROCESSES; ESTIMATOR; CONVERGENCE; COVARIABLES; GOODNESS; CHECKS; RATES;

D O I：

10.1016/j.jmva.2008.04.008

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We developed two kernel smoothing based tests of a parametric mean-regression model against a nonparametric alternative when the response variable is right-censored. The new test statistics are inspired by the synthetic data and the weighted least squares approaches for estimating the parameters of a (non)linear regression model under censoring. The asymptotic critical values of our tests are given by the quantiles of the standard normal law. The tests are consistent against fixed alternatives, local Pitman alternatives and uniformly over alternatives in Holder classes of functions of known regularity. (C) 2008 Elsevier Inc. All rights reserved.

引用

页码：210 / 230

页数：21

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