Turkiye Klinikleri Journal of Biostatistics

.: ORIGINAL RESEARCH
Re-evaluating the Monte Carlo Simulation Results by Using Graphical Techniques
Monte Carlo Simülasyon Sonuçlarının Grafik Teknikler Kullanılarak Yeniden Değerlendirilmesi
Mehmet MENDEŞa
aDepartment of Biometry and Genetics, Çanakkale Onsekiz Mart University Faculty of Agriculture, Çanakkale, TURKEY
Turkiye Klinikleri J Biostat. 2021;13(1):28-38
doi: 10.5336/biostatic.2020-78896
Article Language: EN
Full Text
ABSTRACT
 Objective: When results of simulation studies are examined, it is seen that there are noticeable differences among the simulation results due to differences in the experimental conditions. This study aim at re-evaluating the results of 35 simulation studies carried out the same purpose by using graphic techniques. Material and Methods: Type I error estimates of 35 simulation studies carried out under different experimental conditions (pairing type, variance ratio, sample size ratio, number of simulation, skewness, kurtosis, total sample size, equality of sample size, and number of group) were used as a material of this study. Two different graphical techniques, namely Automatic Linear Modeling and Regression Tree Analysis were used in evaluating Type I error estimates of these studies. Results: Statistical analyses results indicated that the results of simulation studies were affected by different factors and these factors should be considered in order to get more reliable and stable estimates. It was observed that the most important factors affecting Type I error estimates were as pairing type, variance ratio, number of simulation, and sample size ratio. Therefore, these factors can be considered the primary factors that might cause getting different results among the studies. Conclusion: Both methods are promising and can be used efficiently to determine the factors that affect the response variable when there is a large and complex data set.

Keywords: Classification and regression trees; data mining; interaction; simulation
ÖZET
 Amaç: Simülasyon çalışmalarının sonuçları incelendiğinde, deneysel koşullardaki farklılıklardan dolayı simülasyon sonuçları arasında önemli farklılıklar olduğu görülmektedir. Bu çalışma, aynı amaçla yürütülen 35 simülasyon çalışmasının sonuçlarının, grafik teknikler kullanılarak yeniden değerlendirilmesini amaçlamaktadır. Gereç ve Yöntemler: Eşleştirme tipi, varyans oranı, örnek genişliği oranı, simülasyon sayısı, eğrilik ve diklik katsayıları, toplam örnek genişliği, örnek genişliğinin eşitliği ve grup sayısı gibi farklı deneysel koşullarda gerçekleştirilen 35 simülasyon çalışmasının Tip I hata tahminleri, bu çalışmanın materyali olarak kullanılmıştır. Veri setinin analizinde, Otomatik Doğrusal Modelleme ve Regresyon Ağacı Analizi olmak üzere 2 farklı grafik tekniği kullanılmıştır. Bulgular: Yapılan istatistiksel analiz sonucunda, dikkate alınan simülasyon çalışmaları sonuçlarının farklı faktörlerden etkilendiğini, daha güvenilir ve istikrarlı tahminlerin elde edilebilmesi için tespit edilen faktörlerin dikkate alınması gerektiğini göstermiştir. Tip I hata tahminlerini etkileyen en önemli faktörlerin ise sırasıyla eşleştirme tipi ya da varyans oranları ile örnek genişlikleri arasındaki ilişkiler, varyans oranı, çalışmada dikkate alınan simülasyon sayısı ve örneklem büyüklüğü oranı olduğu görülmüştür. Bu bulgulardan hareketle bu 4 faktörün, çalışmada dikkate alınan 35 simülasyon çalışması sonucunda elde edilen Tip I hata olasılıklarının farklılaşmasında başlıca rol oynayan faktörler oldukları sonucuna varılabilir. Sonuç: Her iki yöntem de umut vericidir, büyük ve karmaşık bir veri kümesi olduğunda yanıt değişkenini etkileyen faktörleri belirlemek için verimli bir şekilde kullanılabilir.

Anahtar Kelimeler: Sınıflandırma ve regresyon ağaçları; veri madenciliği; etkileşim; simülasyon
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