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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/64129

    Title: Confidence intervals for the mean of a population containing many zero values under unequal-probability sampling
    Authors: Chen, Hanfeng;Chen, Jiahua;Chen, Shun-Yi
    Contributors: 淡江大學數學學系
    Keywords: Accounting;inclusion probability;mixture models;pseudo-likelihood;stratified sampling;survey sampling;zero-inflated data
    Date: 2010-12
    Issue Date: 2011-10-20 12:44:40 (UTC+8)
    Publisher: Hoboken: Wiley-Blackwell Publishing, Inc.
    Abstract: In many applications, a finite population contains a large proportion of zero values that make the population distribution severely skewed. An unequal-probability sampling plan compounds the problem, and as a result the normal approximation to the distribution of various estimators has poor precision. The central-limit-theorem-based confidence intervals for the population mean are hence unsatisfactory. Complex designs also make it hard to pin down useful likelihood functions, hence a direct likelihood approach is not an option. In this paper, we propose a pseudo-likelihood approach. The proposed pseudo-log-likelihood function is an unbiased estimator of the log-likelihood function when the entire population is sampled. Simulations have been carried out. When the inclusion probabilities are related to the unit values, the pseudo-likelihood intervals are superior to existing methods in terms of the coverage probability, the balance of non-coverage rates on the lower and upper sides, and the interval length. An application with a data set from the Canadian Labour Force Survey-2000 also shows that the pseudo-likelihood method performs more appropriately than other methods.
    Relation: The Canadian Journal of Statistics 38(4), pp.582-597
    DOI: 10.1002/cjs.10077
    Appears in Collections:[Graduate Institute & Department of Mathematics] Journal Article

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