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Dergiler / European Journal of Pure and Applied Mathematics (elektronik) / 2008 / Cilt: 1 - Sayı: 1

Multivariate regression models with power exponential random errors and subset selection using genetic algorithms with information complexity

Minhui LİU
Hamparsum BOZDOGAN
Sayfa
4–37
Yayın Tarihi
2022-10-18
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

A genetic algorithm (GA) approach is used to obtain the estimates of the model parameters and to carry out the subset selection of the best predictors under these two different model types.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:In this paper we introduce and develop two different novel multivariate regression models with Power Exponential (PE) random errors for the first time.
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Temel Bulgular & Veri:Our first model assumes that the observations are independent and the second model assumes that the observations are dependent.
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Sonuç & Katkı:A genetic algorithm (GA) approach is used to obtain the estimates of the model parameters and to carry out the subset selection of the best predictors under these two different model types.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Abstract

In this paper we introduce and develop two different novel multivariate regression models with Power Exponential (PE) random errors for the first time. Our first model assumes that the observations are independent and the second model assumes that the observations are dependent. These two models coincide only when the shape parameter of the multivariate Power Exponential (MPE) distribution is equal to one which corresponds to the multivariate normal distribution. We develop method of moments (MOM) and the maximum likelihood (ML) methods to estimate the model parameters. The model selection criteria such as AIC and ICOMP(IFIM) for both models are derived. Two simulation examples and a real example on a benchmark data set are given to show the applications of these two models in subset selection of the best predictors. A genetic algorithm (GA) approach is used to obtain the estimates of the model parameters and to carry out the subset selection of the best predictors under these two different model types.

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Bu Makaleye Atıf Yap

LİU, M., BOZDOGAN, H. (2022). Multivariate regression models with power exponential random errors and subset selection using genetic algorithms with information complexity. European Journal of Pure and Applied Mathematics (elektronik), Cilt: 1 - Sayı: 1, 4–37. https://ajindex.com/articles/511435

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Kaynakça (14)

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