Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/10195
Título: Optimization in Generalized Linear Models: a Case Study
Autor: Silva, Eliana Costa e
Correia, Aldina
Lopes, Isabel Cristina
Palavras-chave: Nonlinear Optimization
Generalized Linear Models
Gamma Distribution
Water Quality
Data: 2016
Editora: American Institute of Physics
Resumo: The maximum likelihood method is usually chosen to estimate the regression parameters of Generalized Linear Models (GLM) and also for hypothesis testing and goodness of fit tests. The classical method for estimating GLM parameters is the Fisher scores. In this work we propose to compute the estimates of the parameters with two alternative methods: a derivative-based optimization method, namely the BFGS method which is one of the most popular of the quasi-Newton algorithms, and the PSwarm derivative-free optimization method that combines features of a pattern search optimization method with a global Particle Swarm scheme. As a case study we use a dataset of biological parameters (phytoplankton) and chemical and environmental parameters of the water column of a Portuguese reservoir. The results show that, for this dataset, BFGS and PSwarm methods provided a better fit, than Fisher scores method, and can be good alternatives for finding the estimates for the parameters of a GLM.
URI: http://hdl.handle.net/10400.22/10195
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