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Confidence intervals for data containing many zeros and ones based on empirical likelihood-type methods. (English) Zbl 07481517

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.

MSC:

62-XX Statistics
Full Text: DOI

References:

[1] Cox, DR; Snell, EJ., On sampling and the estimation of rare errors, Biometrika, 66, 125-132 (1979) · Zbl 0403.62017
[2] Tamura, H., Estimation of rare errors using expert judgement, Biometrika, 75, 1-9 (1988) · Zbl 0644.62033
[3] Kvanli, AH; Shen, YK; Deng, LY., Construction of confidence intervals for the mean of a population containing many zero values, J Bus Econom Stat, 16, 362-368 (1998)
[4] Zhou, XH; Tu, W., Confidence intervals for the mean of diagnostic test charge data containing zeros, Biometrics, 56, 1118-1125 (2000) · Zbl 1060.62686
[5] Chen, J.; Chen, S-Y; Rao, JNK., Empirical likelihood confidence intervals for a population containing many zero values, Can J Stat, 31, 1, 53-68 (2003) · Zbl 1035.62007
[6] Chen, SX; Qin, J., Empirical likelihood-based confidence intervals for data with possible zero observations, Stat Probab Lett, 65, 29-37 (2003) · Zbl 1116.62345
[7] Owen, AB., Empirical likelihood ratio confidence intervals for a single functional, Biometrika, 75, 237-249 (1988) · Zbl 0641.62032
[8] Owen, AB., Empirical likelihood confidence regions, Ann Stat, 18, 90-120 (1990) · Zbl 0712.62040
[9] Owen, AB., Empirical likelihood for linear models, Ann Stat, 19, 1725-1747 (1991) · Zbl 0799.62048
[10] Owen, AB., Empirical likelihood (2001), New York: Champan & Hall, New York · Zbl 0989.62019
[11] Tsao, M., Extending the empirical likelihood by domain expansion, Can J Stat, 41, 2, 257-274 (2013) · Zbl 1273.62076
[12] Chen, J.; Variyath, AM; Abraham, B., Adjusted empirical likelihood and its properties, J Comput Graph Stat, 17, 2, 426-443 (2008)
[13] Jing, B-Y; Tsao, M.; Zhou, W., Transforming the empirical likelihood towards better accuracy, Can J Stat, 45, 3, 340-352 (2017) · Zbl 1474.62155
[14] Bao, J.; Vinciotti, V.; Wit, E.; ’t Hoen, PA., Joint modeling of ChIP-seq data via a Markov random field model, Biostatistics, 15, 2, 296-310 (2014)
[15] Melkersson, M.; Rooth, DO., Modeling female fertility using inflated count data models, J Popul Econ, 13, 2, 189-203 (2000)
[16] Stewart, P.A generalized inflated poisson distribution [master’s thesis]. Marshall University, Huntington, VA; 2014.
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