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Information Journal Paper

Title

ON IDENTIFIABLITY IN WEIGHTED DISTRIBUTIONS USING GENERALIZED MAXIMUM LIKELIHOOD ESTIMATION

Pages

  73-84

Abstract

 In this research, the MAXIMUM LIKELIHOOD/fa?page=1&sort=1&ftyp=all&fgrp=all&fyrs=all" target="_blank"> GENERALIZED MAXIMUM LIKELIHOOD estimator (GMLE) is used to investigate the parameters estimation for WEIGHTED DISTRIBUTIONs. There exist situations where the random sample from the population of interest is not available due to the data having unequal probabilities of entering the sample. The method of WEIGHTED DISTRIBUTIONs models the certainty of the probabilities of the events as observed and recorded. It is shown that if the mechanism of SAMPLE SELECTION is known up to one unknown parameter, the MAXIMUM LIKELIHOOD estimator (MLE) would be unidentifiable when the CONJUGATE WEIGHT function is used. This problem is solved by addition of a prior distribution on model parameters yielding the GMLEs which are identifiable. We also propose the GMLEs for negative exponential, normal and Poisson WEIGHTED DISTRIBUTIONs when MLEs are unidentifiable.

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    APA: Copy

    ZADKARAMI, M.R.. (2008). ON IDENTIFIABLITY IN WEIGHTED DISTRIBUTIONS USING GENERALIZED MAXIMUM LIKELIHOOD ESTIMATION. JOURNAL OF THE IRANIAN STATISTICAL SOCIETY (JIRSS), 7(1-2), 73-84. SID. https://sid.ir/paper/117880/en

    Vancouver: Copy

    ZADKARAMI M.R.. ON IDENTIFIABLITY IN WEIGHTED DISTRIBUTIONS USING GENERALIZED MAXIMUM LIKELIHOOD ESTIMATION. JOURNAL OF THE IRANIAN STATISTICAL SOCIETY (JIRSS)[Internet]. 2008;7(1-2):73-84. Available from: https://sid.ir/paper/117880/en

    IEEE: Copy

    M.R. ZADKARAMI, “ON IDENTIFIABLITY IN WEIGHTED DISTRIBUTIONS USING GENERALIZED MAXIMUM LIKELIHOOD ESTIMATION,” JOURNAL OF THE IRANIAN STATISTICAL SOCIETY (JIRSS), vol. 7, no. 1-2, pp. 73–84, 2008, [Online]. Available: https://sid.ir/paper/117880/en

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