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Confidence intervals for data containing many zeros and ones based on empirical likelihood-type methods.
Faculty Author(s): Stewart, Patrick
Student Author(s): -
Department: MATH
Publication: Journal of Statistical Computation and Simulation
Year: 2020
Abstract: Summary: ``In this paper, several existing data-driven nonparametric methods including empirical likelihood, adjusted empirical likelihood and transformed empirical likelihood are considered to construct confidence intervals for the mean of a population containing many zeros and ones. Meanwhile, we propose a transformed adjusted empirical likelihood which combines the merits of adjusted and transformed empirical likelihoods. All five methods are compared to normal approximation in terms of coverage probabilities under various scenarios through simulations. All methods are applied to three datasets to illustrate the procedure of obtaining confidence intervals.''
Link: Confidence intervals for data containing many zeros and ones based on empirical likelihood-type methods.