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Information Journal Paper

Title

INFERENCE OF SPATIAL GENERALIZED LINEAR MIXED MODELS USING INTEGRATED LAPLACE NESTED APPROXIMATION

Pages

  55-69

Abstract

 SPATIAL GENERALIZED LINEAR MIXED MODELs are used for modeling geostatistical discrete spatial responses and spatial correlation of the data is considered via latent variables. The most important interest in these models is estimation of the parameters and prediction of the latent variables. In this paper, first, a prediction method is presented. Then a Bayesian approach and MCMC algorithms are proposed. Since these models are complicated and Monte Carlo sampling is used in the Bayesian inference of these models, computation time is long. In order to resolve this problem, the Approximate Bayesian methods are considered. Finally, the proposed methods are applied to a case study on rainfall data observed in the weather stations of Semnan in 1391.

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    APA: Copy

    HOSSEINI, FATEMEH, KARIMI, OMID, & MOHAMMAD KARIMI, MONAVAR. (2014). INFERENCE OF SPATIAL GENERALIZED LINEAR MIXED MODELS USING INTEGRATED LAPLACE NESTED APPROXIMATION. ADVANCES IN MATHEMATICAL MODELING, 4(1), 55-69. SID. https://sid.ir/paper/243870/en

    Vancouver: Copy

    HOSSEINI FATEMEH, KARIMI OMID, MOHAMMAD KARIMI MONAVAR. INFERENCE OF SPATIAL GENERALIZED LINEAR MIXED MODELS USING INTEGRATED LAPLACE NESTED APPROXIMATION. ADVANCES IN MATHEMATICAL MODELING[Internet]. 2014;4(1):55-69. Available from: https://sid.ir/paper/243870/en

    IEEE: Copy

    FATEMEH HOSSEINI, OMID KARIMI, and MONAVAR MOHAMMAD KARIMI, “INFERENCE OF SPATIAL GENERALIZED LINEAR MIXED MODELS USING INTEGRATED LAPLACE NESTED APPROXIMATION,” ADVANCES IN MATHEMATICAL MODELING, vol. 4, no. 1, pp. 55–69, 2014, [Online]. Available: https://sid.ir/paper/243870/en

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