scholarly journals Empirical likelihood confidence regions of the parameters in a partially single-index varying-coefficient model

2019 ◽  
Vol 17 (1) ◽  
pp. 728-741
Author(s):  
Xing Xiang ◽  
Wanrong Liu

Abstract In this paper, we investigate a partially single-index varying-coefficient model, and suggest two empirical log-likelihood ratio statistics for the unknown parameters in the model. The first statistic is asymptotically distributed as a weighted sum of independent chi-square variables under some mild conditions. It is proved that another statistic, with adjustment factor, is asymptotically standard chi-square under some suitable conditions. These useful statistics could be used to construct the confidence regions of the parameters. A simulation study indicates that, with the increase of sample size, the coverage probability of the confidence region constructed by us gradually approaches the theoretical value.

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Yunquan Song ◽  
Ling Jian ◽  
Lu Lin

In this paper, we consider a single-index varying-coefficient model with application to longitudinal data. In order to accommodate the within-group correlation, we apply the block empirical likelihood procedure to longitudinal single-index varying-coefficient model, and prove a nonparametric version of Wilks’ theorem which can be used to construct the block empirical likelihood confidence region with asymptotically correct coverage probability for the parametric component. In comparison with normal approximations, the proposed method does not require a consistent estimator for the asymptotic covariance matrix, making it easier to conduct inference for the model's parametric component. Simulations demonstrate how the proposed method works.


Biometrics ◽  
2016 ◽  
Vol 72 (4) ◽  
pp. 1275-1284 ◽  
Author(s):  
Xinchao Luo ◽  
Lixing Zhu ◽  
Hongtu Zhu

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