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Activity Number: 544 - Winners: Business and Economic Statistics Student Paper Awards
Type: Topic Contributed
Date/Time: Thursday, August 6, 2020 : 1:00 PM to 2:50 PM
Sponsor: Business and Economic Statistics Section
Abstract #309894
Title: High Order Adjusted Block-Wise Empirical Likelihood for Weakly Dependent Data
Author(s): Guangxing Wang* and Wolfgang Polonik
Companies: University of California-Davis and University of California, Davis
Keywords: Weakly Dependent Data; Adjusted Empirical Likelihood; Bartlett Correction; Empirical Likelihood Ratio Confidence Region; Convex Hull Constraint
Abstract:

The upper limit on the coverage probability of the empirical likelihood ratio confidence region severely hampers its application in statistical inferences. The root cause of this upper limit is the convex hull of the estimating functions that is used in the construction of the profile empirical likelihood. For i.i.d data, various methods have been proposed to solve this issue by modifying the convex hull, but it is not clear how well these methods perform when the data is no longer independent. In this paper, we consider weakly dependent multivariate data, and we combine the block-wise empirical likelihood with the adjusted empirical likelihood to tackle data dependency and the convex hull constraint simultaneously. We show that our method not only preserves the much celebrated asymptotic chi-square distribution, but also improves the coverage probability by removing the upper limit. Further, we show that our method is also Bartlett correctable, thus is able to achiev high order asymptotic coverage accuracy.


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