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Split sample empirical likelihood

Author

Listed:
  • Jaeger, Adam
  • Lazar, Nicole A.

Abstract

Empirical likelihood offers a nonparametric approach to estimation and inference, which replaces the probability density-based likelihood function with a function defined by estimating equations. While this eliminates the need for a parametric specification, the restriction of numerical optimization greatly decreases the applicability of empirical likelihood for large data problems. A solution to this problem is the split sample empirical likelihood; this variant utilizes a divide and conquer approach, allowing for parallel computation of the empirical likelihood function. The results show the asymptotic distribution of the estimators and test statistics derived from the split sample empirical likelihood are the same seen in standard empirical likelihood yet have significantly decreased computational times.

Suggested Citation

  • Jaeger, Adam & Lazar, Nicole A., 2020. "Split sample empirical likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
    • Handle: RePEc:eee:csdana:v:150:y:2020:i:c:s0167947320300852
    DOI: 10.1016/j.csda.2020.106994
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    References listed on IDEAS

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    1. Shi, Jian & Lau, Tai-Shing, 2000. "Empirical Likelihood for Partially Linear Models," Journal of Multivariate Analysis, Elsevier, vol. 72(1), pages 132-148, January.
    2. Nicole A. Lazar, 2003. "Bayesian empirical likelihood," Biometrika, Biometrika Trust, vol. 90(2), pages 319-326, June.
    3. Grendar, Marian & Judge, George G., 2010. "Revised empirical likelihood," CUDARE Working Paper Series 1106, University of California at Berkeley, Department of Agricultural and Resource Economics and Policy.
    4. Grendar, Marian & Judge, George G., 2010. "Revised empirical likelihood," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt6gs579r0, Department of Agricultural & Resource Economics, UC Berkeley.
    5. Jing Qin & Biao Zhang, 2007. "Empirical‐likelihood‐based inference in missing response problems and its application in observational studies," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(1), pages 101-122, February.
    6. Qifan Song & Faming Liang, 2015. "A split-and-merge Bayesian variable selection approach for ultrahigh dimensional regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(5), pages 947-972, November.
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