Determination of Best Variable Set for Count Models by Particle Swarm Optimization

Year 2019, Volume: 23 Issue: Special [en], 76 - 83, 01.03.2019

Haydar Koç Tuba Koç Emre Dünder

https://doi.org/10.19113/sdufenbed.436178

Abstract

In most scientific
studies quantitative data are used which take non-negative integer values,
called count data. Count data are also used frequently in the context of
regression analysis, which is one of the most basic analysis methods of
statistical analysis. The regression models in which the dependent variable can
be expressed by integers are defined as count models. In this study, the model
selection in the context of count models was investigated by using classical
selection methods and PSO algorithm. Applications were made on both simulation
and real data. As a result, it has been shown that PSO algorithm can be used as
an alternative method for PSO algorithm selection for count models when the
number of model variables increases and the correlation values ​​between
independent variables increases as compared to classical methods.

Keywords

Variable selection, Count models, Heuristic optimization

Parçacık Sürü Optimizasyonu Yöntemi ile Sayım Modelleri için En Uygun Değişken Kümesinin Belirlenmesi

Abstract

Birçok
bilimsel çalışmada sayım verisi olarak adlandırılan negatif olmayan tamsayı
değerleri alan nicel veriler kullanılmaktadır. İstatistiğin en temel analiz yöntemlerinden biri olan regresyon
analizi kapsamında da sayım verileri oldukça sık kullanılmaktadır. Bağımlı
değişkenin tamsayı ile ifade edilebildiği regresyon modelleri sayım modelleri
olarak tanımlanır. Bu çalışmada sayım modelleri kapsamında model seçimi
incelendi. Sayım modellerinde model seçimi için klasik seçim yöntemleri ve PSO
algoritması kullanıldı. Uygulamalar hem simülasyon hem de gerçek veriler
üzerinde yapıldı. Sonuç olarak klasik yöntemlerle kıyaslandığında PSO
algoritmasının, modeldeki değişken sayısı arttıkça ve bağımsız değişkenler
arasındaki korelasyon değerleri yükseldikçe daha iyi sonuçlar verdiği ve sayım
modelleri için PSO algoritmasının değişken seçiminde alternatif bir yöntem
olarak kullanılabileceği gösterilmiştir.

Keywords

Değişken Seçimi, Sayım modelleri, Sezgisel Optimizasyon

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