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

Bircok bilimsel calismada sayim verisi olarak adlandirilan negatif olmayan tamsayi degerleri alan nicel veriler kullanilmaktadir . Istatistigin en temel analiz yontemlerinden biri olan regresyon analizi kapsaminda da sayim verileri oldukca sik kullanilmaktadir. Bagimli degiskenin tamsayi ile ifade edilebildigi regresyon modelleri sayim modelleri olarak tanimlanir. Bu calismada sayim modelleri kapsaminda model secimi incelendi. Sayim modellerinde model secimi icin klasik secim yontemleri ve PSO algoritmasi kullanildi. Uygulamalar hem simulasyon hem de gercek veriler uzerinde yapildi. Sonuc olarak klasik yontemlerle kiyaslandiginda PSO algoritmasinin, modeldeki degisken sayisi arttikca ve bagimsiz degiskenler arasindaki korelasyon degerleri yukseldikce daha iyi sonuclar verdigi ve sayim modelleri icin PSO algoritmasinin degisken seciminde alternatif bir yontem olarak kullanilabilecegi gosterilmistir.

• Publication year

  2019

• Venue

  Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi

• Publication date

  2019-03-01

• Fields of study

  Not labeled

• Identifiers
  DOI 10.19113/SDUFENBED.436178
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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