Genetic algorithms for outlier detection and variable selection in linear regression models

DOI10.1007/S00500-003-0310-2zbMath1061.62103OpenAlexW2076877989MaRDI QIDQ1769647

J. Tolvi

Publication date: 4 April 2005

Published in: Soft Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00500-003-0310-2

zbMATH Keywords

model selection

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Approximation methods and heuristics in mathematical programming (90C59)

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A new correction approach for information criteria to detect outliers in regression modeling ⋮ Identification of influential observations based on binary particle swarm optimization in the cox PH model ⋮ Bayesian variable selection in generalized linear models using a combination of stochastic optimization methods ⋮ Genetic algorithms for outlier detection in multiple regression with different information criteria ⋮ Predicting EU energy industry excess returns on EU market index via a constrained genetic algorithm ⋮ Forecasting container throughput of Qingdao Port with a hybrid model

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