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Information Journal Paper

Title

NEW RESULTS ON IDENTIFIABILITY OF GENERALIZED LINEAR MODELS WITH RANDOM EFFECTS

Pages

  49-69

Abstract

 Identifiablity is a necessary property for the adequacy of a statistical model. When a model is not identifiable, no amount of data cannot determine true parameter. In this article, well-known concept of identifiablity and its properties is reviewed. Moreover, since non-identifiably problem in LINEAR MIXED EFFECTS MODELS and generalized linear models with random effects is very common, our main focus is on these models. On the other hand, statistical software, after fitting non-identifiable models, don’t usually indicate the problem and show invalid outputs. Consequently, it is useful to have a way to check model IDENTIFIABILITY before fitting. In this regard, some new theorems to check IDENTIFIABILITY in generalized linear models with random effects are presented. data from non-identifiable models are simulated and problems with model non-identifiablity are listed for showing advantages of the mentioned theorems.

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    APA: Copy

    TABRIZI, ELHAM, BAHRAMI SAMANI, EHSAN, & JAFARI, NASEH. (2015). NEW RESULTS ON IDENTIFIABILITY OF GENERALIZED LINEAR MODELS WITH RANDOM EFFECTS. ADVANCES IN MATHEMATICAL MODELING, 4(2), 49-69. SID. https://sid.ir/paper/243896/en

    Vancouver: Copy

    TABRIZI ELHAM, BAHRAMI SAMANI EHSAN, JAFARI NASEH. NEW RESULTS ON IDENTIFIABILITY OF GENERALIZED LINEAR MODELS WITH RANDOM EFFECTS. ADVANCES IN MATHEMATICAL MODELING[Internet]. 2015;4(2):49-69. Available from: https://sid.ir/paper/243896/en

    IEEE: Copy

    ELHAM TABRIZI, EHSAN BAHRAMI SAMANI, and NASEH JAFARI, “NEW RESULTS ON IDENTIFIABILITY OF GENERALIZED LINEAR MODELS WITH RANDOM EFFECTS,” ADVANCES IN MATHEMATICAL MODELING, vol. 4, no. 2, pp. 49–69, 2015, [Online]. Available: https://sid.ir/paper/243896/en

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