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Information Journal Paper

Title

VARIABLE SELECTION OF GENERALIZED SEMI-PARAMETRIC MIXTURE MODELS

Pages

  1-26

Abstract

 The purpose of this paper is identifying best covariates of a SEMI-PARAMETRIC MODEL in the presence of penalized coefficients. It should be noted that in each model, coefficients of the existing variables is considered as a combination of parameters where some of them affect the response variable linearly and some of them functionally. So, semi-parametric method was considered as an optimum solution. In this paper, we concerned with VARIABLE SELECTION in finite mixture of generalized SEMI-PARAMETRIC MODELs. This task consists of model selection for nonparametric component and VARIABLE SELECTION for parametric part. Thus, we encounter with separate model selection for each nonparametric component of each sub model. To overcome to this computational burden, we introduce a class of VARIABLE SELECTION procedures for finite mixture of generalized SEMI-PARAMETRIC MODELs. It is shown that the new method is consistent for VARIABLE SELECTION. Simulations show that the performance of proposed method is good and improve pervious works in this area and requires much less computing power than existing methods.

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  • Cite

    APA: Copy

    ESKANDARI, FARZAD, ARMAZ, EHSAN, & FARNOOSH, RAHMAN. (2014). VARIABLE SELECTION OF GENERALIZED SEMI-PARAMETRIC MIXTURE MODELS. ADVANCES IN MATHEMATICAL MODELING, 4(1), 1-26. SID. https://sid.ir/paper/243866/en

    Vancouver: Copy

    ESKANDARI FARZAD, ARMAZ EHSAN, FARNOOSH RAHMAN. VARIABLE SELECTION OF GENERALIZED SEMI-PARAMETRIC MIXTURE MODELS. ADVANCES IN MATHEMATICAL MODELING[Internet]. 2014;4(1):1-26. Available from: https://sid.ir/paper/243866/en

    IEEE: Copy

    FARZAD ESKANDARI, EHSAN ARMAZ, and RAHMAN FARNOOSH, “VARIABLE SELECTION OF GENERALIZED SEMI-PARAMETRIC MIXTURE MODELS,” ADVANCES IN MATHEMATICAL MODELING, vol. 4, no. 1, pp. 1–26, 2014, [Online]. Available: https://sid.ir/paper/243866/en

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