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Information Journal Paper

Title

IMPROVING OF STRUCTURED MARKOV CHAIN MONTE CARLO ALGORITHM IN MULTILEVEL MODELS

Pages

  35-58

Abstract

 The multilevel models are used in applied sciences including social sciences, sociology, medicine, economic for analysing correlated data. There are various approaches to estimate the model parameters when the responses are normally distributed. To implement the Bayesian approach, a generalized version of the Markov Chain Monte Carlo algorithm, which has a simple structure and removes the correlations among the simulated samples for the fixed parameters and the errors in higher levels, is used in this article. Because the dimension of the covariance matrix for the new error vector is increased, based upon the CHOLESKY DECOMPOSITION of the covariance matrix, two methods are proposed to speed the convergence of this approach. Then, the performances of these methods are evaluated in a simulation study and real life data.

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    APA: Copy

    FAROKHI, A., & GOLALIZADEH, M.. (2010). IMPROVING OF STRUCTURED MARKOV CHAIN MONTE CARLO ALGORITHM IN MULTILEVEL MODELS. JOURNAL OF STATISTICAL SCIENCES, 4(1), 35-58. SID. https://sid.ir/paper/124113/en

    Vancouver: Copy

    FAROKHI A., GOLALIZADEH M.. IMPROVING OF STRUCTURED MARKOV CHAIN MONTE CARLO ALGORITHM IN MULTILEVEL MODELS. JOURNAL OF STATISTICAL SCIENCES[Internet]. 2010;4(1):35-58. Available from: https://sid.ir/paper/124113/en

    IEEE: Copy

    A. FAROKHI, and M. GOLALIZADEH, “IMPROVING OF STRUCTURED MARKOV CHAIN MONTE CARLO ALGORITHM IN MULTILEVEL MODELS,” JOURNAL OF STATISTICAL SCIENCES, vol. 4, no. 1, pp. 35–58, 2010, [Online]. Available: https://sid.ir/paper/124113/en

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