Variable selection in gamma regression models via artificial bee colony algorithm

DOI10.1080/02664763.2016.1254730OpenAlexW2550009637MaRDI QIDQ5139080

Mehmet Ali Cengiz, Emre Dünder, Serpil Gümüştekin

Publication date: 7 December 2020

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2016.1254730

zbMATH Keywords

heuristic optimizationvariable selectionartificial bee colony algorithmgamma regression analysisR-project

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (3)

Modified ridge-type estimator for the gamma regression model ⋮ Liu-type estimator for the gamma regression model ⋮ Modified ridge-type estimator for the inverse Gaussian regression model

Uses Software

• alr3
• Glmulti
• ABC
• bestglm
• ALDqr
• PERregress

Cites Work

• Unnamed Item
• Subset selection in Poisson regression
• Subset selection in multiple linear regression models: a hybrid of genetic and simulated annealing algorithms
• A variable selection method based on Tabu search for logistic regression models
• Subset selection in multiple linear regression in the presence of outlier and multicollinearity
• A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm
• A New Framework for Optimization Based-On Hybrid Swarm Intelligence
• Variable Selection in Logistic Regression Models
• On variable selection in generalized linear and related regression models
• Tabu search model selection in multiple regression analysis
• The Variable Selection Problem
• Variable selection for varying dispersion beta regression model

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