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A penalized version of the empirical likelihood ratio for the population mean

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  • Bartolucci, Francesco

Abstract

A penalized version of the empirical likelihood ratio test statistic for the population mean is proposed which may be computed even when this parameter does not belong to the convex hull of the data. Some theoretical results on the proposed test statistic are provided together with some guidelines on the choice of the penalization term. A double bootstrap procedure is also described which may be used for calibration when the sample size is small. The approach is illustrated by an example and a small simulation study.

Suggested Citation

  • Bartolucci, Francesco, 2007. "A penalized version of the empirical likelihood ratio for the population mean," Statistics & Probability Letters, Elsevier, vol. 77(1), pages 104-110, January.
    • Handle: RePEc:eee:stapro:v:77:y:2007:i:1:p:104-110
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    Cited by:

    1. Kévin Beaubrun-Diant & Tristan-Pierre Maury, 2011. "Assessing the Interaction between Real Estate and Equity in Households Portfolio Choice," Working Papers halshs-00635582, HAL.
    2. Min Tsao & Fan Wu, 2014. "Extended empirical likelihood for estimating equations," Biometrika, Biometrika Trust, vol. 101(3), pages 703-710.
    3. Roberto Baragona & Francesco Battaglia & Domenico Cucina, 2017. "Empirical likelihood ratio in penalty form and the convex hull problem," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(4), pages 507-529, November.
    4. E. Beaubrun-Diant, Kevin. & Maury, Tristan-Pierre, 2016. "Home tenure, stock market participation, and composition of the household portfolio," Journal of Housing Economics, Elsevier, vol. 32(C), pages 1-17.
    5. Mahdieh Bayati & Seyed Kamran Ghoreishi & Jingjing Wu, 2021. "Bayesian analysis of restricted penalized empirical likelihood," Computational Statistics, Springer, vol. 36(2), pages 1321-1339, June.
    6. repec:dau:papers:123456789/7331 is not listed on IDEAS
    7. Thorne Thomas, 2015. "Empirical likelihood tests for nonparametric detection of differential expression from RNA-seq data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(6), pages 575-583, December.
    8. Roberto Baragona & Francesco Battaglia & Domenico Cucina, 2016. "Empirical Likelihood for Outlier Detection and Estimation in Autoregressive Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(3), pages 315-336, May.
    9. Xianyang Zhang & Xiaofeng Shao, 2016. "On the coverage bound problem of empirical likelihood methods for time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 395-421, March.

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