Modified empirical likelihood-based confidence intervals for data containing many zero observations

Patrick Stewart and Wei Ning ()
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Patrick Stewart: Bowling Green State University
Wei Ning: Bowling Green State University

Computational Statistics, 2020, vol. 35, issue 4, No 21, 2019-2042

Abstract: Abstract Data containing many zeroes is popular in statistical applications, such as survey data. A confidence interval based on the traditional normal approximation may lead to poor coverage probabilities, especially when the nonzero values are highly skewed and the sample size is small or moderately large. The empirical likelihood (EL), a powerful nonparametric method, was proposed to construct confidence intervals under such a scenario. However, the traditional empirical likelihood experiences the issue of under-coverage problem which causes the coverage probability of the EL-based confidence intervals to be lower than the nominal level, especially in small sample sizes. In this paper, we investigate the numerical performance of three modified versions of the EL: the adjusted empirical likelihood, the transformed empirical likelihood, and the transformed adjusted empirical likelihood for data with various sample sizes and various proportions of zero values. Asymptotic distributions of the likelihood-type statistics have been established as the standard chi-square distribution. Simulations are conducted to compare coverage probabilities with other existing methods under different distributions. Real data has been given to illustrate the procedure of constructing confidence intervals.

Keywords: Zero observations; Adjusted empirical likelihood; Transformed empirical likelihood; Transformed adjusted empirical likelihood; Confidence intervals; Coverage probability (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00180-020-00993-1

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