Fixed-b asymptotics for blockwise empirical likelihood

2014 ◽  
Author(s):  
Xianyang Zhang ◽  
Xiaofeng Shao
Keyword(s):  
Empirical Likelihood ◽  
Blockwise Empirical Likelihood

scholarly journals Empirical Likelihood for Partially Linear Single-Index Models under Negatively Associated Errors

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xin Qi ◽  
ZhuoXi Yu
Keyword(s):  
Empirical Likelihood ◽  
Likelihood Ratio Statistic ◽  
Confidence Regions ◽  
Likelihood Method ◽  
Negatively Associated ◽  
Single Index ◽  
Index Model ◽  
Partially Linear ◽  
Blockwise Empirical Likelihood ◽  
Parameters Of Interest

In this paper, the authors consider the application of the blockwise empirical likelihood method to the partially linear single-index model when the errors are negatively associated, which often exist in sequentially collected economic data. Thereafter, the blockwise empirical likelihood ratio statistic for the parameters of interest is proved to be asymptotically chi-squared. Hence, it can be directly used to construct confidence regions for the parameters of interest. A few simulation experiments are used to illustrate our proposed method.

On empirical likelihood option pricing

2017 ◽  
Vol 19 (5) ◽  
pp. 41-53
Author(s):  
Xiaolong Zhong ◽  
Jie Cao ◽  
Yong Jin ◽  
Wei Zheng
Keyword(s):  
Option Pricing ◽  
Empirical Likelihood

scholarly journals Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Hanji He ◽  
Guangming Deng
Keyword(s):  
Empirical Likelihood ◽  
Missing At Random ◽  
Confidence Regions ◽  
Likelihood Inference ◽  
High Dimensional ◽  
Finite Sample ◽  
The Mean ◽  
Data Missing ◽  
Consistency And Asymptotic Normality ◽  
The Impact

We extend the mean empirical likelihood inference for response mean with data missing at random. The empirical likelihood ratio confidence regions are poor when the response is missing at random, especially when the covariate is high-dimensional and the sample size is small. Hence, we develop three bias-corrected mean empirical likelihood approaches to obtain efficient inference for response mean. As to three bias-corrected estimating equations, we get a new set by producing a pairwise-mean dataset. The method can increase the size of the sample for estimation and reduce the impact of the dimensional curse. Consistency and asymptotic normality of the maximum mean empirical likelihood estimators are established. The finite sample performance of the proposed estimators is presented through simulation, and an application to the Boston Housing dataset is shown.

Sign in / Sign up

Export Citation Format

Share Document