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Mean Empirical Likelihood

Author

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  • Liang, Wei
  • Dai, Hongsheng
  • He, Shuyuan

Abstract

Empirical likelihood methods are widely used in different settings to construct the confidence regions for parameters which satisfy the moment constraints. However, the empirical likelihood ratio confidence regions may have poor accuracy, especially for small sample sizes and multi-dimensional situations. A novel Mean Empirical Likelihood (MEL) method is proposed. A new pseudo dataset using the means of observation values is constructed to define the empirical likelihood ratio and it is proved that this MEL ratio satisfies Wilks’ theorem. Simulations with different examples are given to assess its finite sample performance, which shows that the confidence regions constructed by Mean Empirical Likelihood are much more accurate than that of the other Empirical Likelihood methods.

Suggested Citation

  • Liang, Wei & Dai, Hongsheng & He, Shuyuan, 2019. "Mean Empirical Likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 155-169.
    • Handle: RePEc:eee:csdana:v:138:y:2019:i:c:p:155-169
    DOI: 10.1016/j.csda.2019.04.007
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    References listed on IDEAS

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    1. Whitney K. Newey & Richard J. Smith, 2004. "Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators," Econometrica, Econometric Society, vol. 72(1), pages 219-255, January.
    2. Jing, Bing-Yi & Yuan, Junqing & Zhou, Wang, 2009. "Jackknife Empirical Likelihood," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1224-1232.
    3. Liu, Yukun & Zou, Changliang & Zhang, Runchu, 2008. "Empirical likelihood for the two-sample mean problem," Statistics & Probability Letters, Elsevier, vol. 78(5), pages 548-556, April.
    4. Harrison, David Jr. & Rubinfeld, Daniel L., 1978. "Hedonic housing prices and the demand for clean air," Journal of Environmental Economics and Management, Elsevier, vol. 5(1), pages 81-102, March.
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    Cited by:

    1. Patrick Stewart & Wei Ning, 2020. "Modified empirical likelihood-based confidence intervals for data containing many zero observations," Computational Statistics, Springer, vol. 35(4), pages 2019-2042, December.
    2. Liang, Wei & Dai, Hongsheng, 2021. "Empirical likelihood based on synthetic right censored data," Statistics & Probability Letters, Elsevier, vol. 169(C).

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