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Information Journal Paper

Title

COMPARISON OF ESTIMATES USING RECORD STATISTICS FROM LOMAX MODEL: BAYESIAN AND NON BAYESIAN APPROACHES

Pages

  139-158

Abstract

 This paper addresses the problem of Bayesian estimation of the parameters, reliability and hazard function in the context of record statistics values from the two-parameter LOMAX DISTRIBUTION. The ML and the Bayes estimates based on records are derived for the two unknown parameters and the survival time parameters, reliability and hazard functions. The Bayes estimates are obtained based on conjugate prior for the scale parameter and discrete prior for the shape parameter of this model. This is done with respect to both symmetric loss function (squared error loss), and asymmetric loss function (linear-exponential (LINEX)) loss function. The maximum likelihood and the different Bayes estimates are compared via Monte Carlo simulation study. A practical example consisting of real RECORD VALUES including in the data from an accelerated test on insulating fluid reported by Nelson was used for illustration and comparison. Finally, Bayesian predictive density function, which is necessary to obtain bounds for predictive interval of future record is derived and discussed using a numerical example.

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    APA: Copy

    ABD ELAH, A.H.. (2006). COMPARISON OF ESTIMATES USING RECORD STATISTICS FROM LOMAX MODEL: BAYESIAN AND NON BAYESIAN APPROACHES. JOURNAL OF STATISTICAL RESEARCH OF IRAN (JSRI), 3(2), 139-158. SID. https://sid.ir/paper/129940/en

    Vancouver: Copy

    ABD ELAH A.H.. COMPARISON OF ESTIMATES USING RECORD STATISTICS FROM LOMAX MODEL: BAYESIAN AND NON BAYESIAN APPROACHES. JOURNAL OF STATISTICAL RESEARCH OF IRAN (JSRI)[Internet]. 2006;3(2):139-158. Available from: https://sid.ir/paper/129940/en

    IEEE: Copy

    A.H. ABD ELAH, “COMPARISON OF ESTIMATES USING RECORD STATISTICS FROM LOMAX MODEL: BAYESIAN AND NON BAYESIAN APPROACHES,” JOURNAL OF STATISTICAL RESEARCH OF IRAN (JSRI), vol. 3, no. 2, pp. 139–158, 2006, [Online]. Available: https://sid.ir/paper/129940/en

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