scholarly journals Empirical likelihood confidence intervals for the Gini measure of income inequality

2010 ◽  
Vol 27 (6) ◽  
pp. 1429-1435 ◽  
Author(s):  
Yongsong Qin ◽  
J.N.K. Rao ◽  
Changbao Wu
Keyword(s):  
Income Inequality ◽  
Confidence Intervals ◽  
Empirical Likelihood

Influence function-based empirical likelihood for inference of quantile medical costs with censored data

2019 ◽  
Vol 29 (7) ◽  
pp. 1913-1934
Author(s):  
Jenny Jeyarajah ◽  
Guanhao Wei ◽  
Gengsheng Qin
Keyword(s):  
Confidence Intervals ◽  
Censored Data ◽  
Empirical Likelihood ◽  
Influence Function ◽  
Likelihood Ratio Statistic ◽  
Medical Costs ◽  
Finite Sample ◽  
Chi Square ◽  
Coverage Probabilities ◽  
Interval Lengths

In this paper, we propose empirical likelihood methods based on influence function and Jackknife techniques to construct confidence intervals for quantile medical costs with censored data. We show that the influence function-based empirical log-likelihood ratio statistic for the quantile medical cost has a standard Chi-square distribution as its asymptotic distribution. Simulation studies are conducted to compare coverage probabilities and interval lengths of the proposed empirical likelihood confidence intervals with the existing normal approximation-based confidence intervals for quantile medical costs. The proposed methods are observed to have better finite-sample performances than existing methods. The new methods are also illustrated through a real example.

scholarly journals Confidence Intervals for the Mean Based on Exponential Type Inequalities and Empirical Likelihood

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Sandra Vucane ◽  
Janis Valeinis ◽  
George Luta
Keyword(s):  
Confidence Intervals ◽  
Empirical Likelihood ◽  
Exponential Type ◽  
Likelihood Method ◽  
Interval Length ◽  
Simple Extension ◽  
Dependent Observations ◽  
Special Cases ◽  
Coverage Accuracy ◽  
The Mean

For independent observations, recently, it has been proposed to construct the confidence intervals for the mean using exponential type inequalities. Although this method requires much weaker assumptions than those required by the classical methods, the resulting intervals are usually too large. Still in special cases, one can find some advantage of using bounded and unbounded Bernstein inequalities. In this paper, we discuss the applicability of this approach for dependent data. Moreover, we propose to use the empirical likelihood method both in the case of independent and dependent observations for inference regarding the mean. The advantage of empirical likelihood is its Bartlett correctability and a rather simple extension to the dependent case. Finally, we provide some simulation results comparing these methods with respect to their empirical coverage accuracy and average interval length. At the end, we apply the above described methods for the serial analysis of a gene expression (SAGE) data example.

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