Maximum Likelihood Estimation of Parameters in Generalized Functional Linear Model
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Abstract: (2792 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. |
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Keywords: Covariance operator, Eigenfunctions, Functional regression, Generalized functional linear model, Karhunen–Loève expansion. |
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Full-Text [PDF 1680 kb]
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Type of Study: Research |
Subject:
Special Received: 2019/01/8 | Accepted: 2020/05/21 | Published: 2020/06/6
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