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

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.

Anahtar Kelimeler:

Değişken Seçimi, Sayım modelleri, Sezgisel Optimizasyon

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

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,

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