An Empirical Likelihood Method in a Partially Linear Single-index Model with Right Censored Data

Empirical-likelihood-based inference for the parameters in a partially linear single-index model with randomly censored data is investigated. We introduce an estimated empirical likelihood for the parameters using a synthetic data approach and show that its limiting distribution is a mixture of cent...

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Bibliographic Details
Published in:Acta mathematica Sinica. English series 2012-05, Vol.28 (5), p.1041-1060
Main Authors: Yang, Yi Ping, Xue, Liu Gen, Cheng, Wei Hu
Format: Article
Language:English
Subjects:
Accuracy
Approximation
Confidence
Constraining
Empirical analysis
Mathematical analysis
Mathematical models
Mathematics
Mathematics and Statistics
Norms
Regression analysis
Science
Simulation
Studies
Survival analysis
右删失数据
应用程序
指标模型
有限样本性质
极限分布
混合物
线性
经验似然估计
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Summary:Empirical-likelihood-based inference for the parameters in a partially linear single-index model with randomly censored data is investigated. We introduce an estimated empirical likelihood for the parameters using a synthetic data approach and show that its limiting distribution is a mixture of central chi-squared distribution. To attack this difficulty we propose an adjusted empirical likelihood to achieve the standard X2-1imit. Furthermore, since the index is of norm 1, we use this constraint to reduce the dimension of parameters, which increases the accuracy of the confidence regions. A simulation study is carried out to compare its finite-sample properties with the existing method. An application to a real data set is illustrated.
ISSN:1439-8516
1439-7617
DOI:10.1007/s10114-011-9157-0