Blockwise generalized empirical likelihood inference for non-linear dynamic moment conditions models

2009 ◽  
Vol 12 (2) ◽  
pp. 208-231 ◽  
Author(s):  
Francesco Bravo
Keyword(s):  
Empirical Likelihood ◽  
Likelihood Inference ◽  
Moment Conditions ◽  
Generalized Empirical Likelihood ◽  
Linear Dynamic ◽  
Non Linear ◽  
Dynamic Moment

Generalized Empirical Likelihood-Based Kernel Estimation of Spatially Similar Densities

2020 ◽  
pp. 385-399
Author(s):  
Kuangyu Wen ◽  
Ximing Wu
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Crop Yield ◽  
Empirical Likelihood ◽  
Kernel Estimation ◽  
Moment Conditions ◽  
Spatial Moments ◽  
Generalized Empirical Likelihood ◽  
Spatial Moment ◽  
Spline Basis

This study concerns the estimation of spatially similar densities, each with a small number of observations. To achieve flexibility and improved efficiency, we propose kernel-based estimators that are refined by generalized empirical likelihood probability weights associated with spatial moment conditions. We construct spatial moments based on spline basis functions that facilitate desirable local customization. Monte Carlo simulations demonstrate the good performance of the proposed method. To illustruate its usefulness, we apply this method to the estimation of crop yield distributions that are known to be spatically similar.

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