An empirical likelihood estimate of the finite population correlation coefficient

Sarjinder Singh, Stephen A. Sedory, Jong Min Kim

    Research output: Contribution to journalArticlepeer-review

    2 Scopus citations

    Abstract

    In this article, the problem of the estimation of finite population correlation coefficient is considered using the empirical likelihood method. A new estimator that makes the use of both the known mean and variance of an auxiliary variable is proposed. The percent relative bias and percent relative efficiency of the proposed new estimator with respect to the usual estimator of the correlation coefficient is investigated through extensive simulation study for values of the correlation coefficient from-0.90 to +0.90. The proposed estimator is found to perform better than the simple correlation coefficient from both the bias and relative efficiency points of views, for the population, considered in the investigation. At the end, the proposed estimator has been extended to complex survey designs. Supplementary materials for this article are available online.

    Original languageEnglish (US)
    Pages (from-to)1430-1441
    Number of pages12
    JournalCommunications in Statistics: Simulation and Computation
    Volume43
    Issue number6
    DOIs
    StatePublished - Jan 1 2014

    Keywords

    • Empirical likelihood estimates
    • Estimation of finite population correlation coefficient
    • Solution to nonlinear equations

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