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:: Volume 24, Issue 2 (3-2020) ::
Andishe 2020, 24(2): 43-54 Back to browse issues page
Maximum Likelihood Estimation of Parameters in Generalized Functional Linear Model
Abstract:   (2441 Views)

Sometimes, in practice, data are a function of another variable, which is called functional data. If the scalar response variable is categorical or discrete, and the covariates are functional, then a generalized functional linear model is used to analyze this type of data. In this paper, a truncated generalized functional linear model is studied and a maximum likelihood approach is used to estimate the model parameters. Finally, in a simulation study and two practical examples, the model and methods presented are implemented.

Keywords: Covariance operator, Eigenfunctions, Functional regression, Generalized functional linear model, Karhunen–Loève expansion.
Full-Text [PDF 1680 kb]   (1059 Downloads)    
Type of Study: Research | Subject: Special
Received: 2019/01/8 | Accepted: 2020/05/21 | Published: 2020/06/6
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Maximum Likelihood Estimation of Parameters in Generalized Functional Linear Model. Andishe 2020; 24 (2) :43-54
URL: http://andisheyeamari.irstat.ir/article-1-804-en.html


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Volume 24, Issue 2 (3-2020) Back to browse issues page
مجله اندیشه آماری Andishe _ye Amari
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