A Novel Method for Objective Criterion Weighting: The Neyman Chi-Square Distance Approach (NCDA)

Yıl 2025, Cilt: 37 Sayı: 4, 444 - 469, 23.12.2025

Furkan Fahri Altıntaş

https://doi.org/10.7240/jeps.1782364

Öz

In the context of Multi-Criteria Decision-Making (MCDM), most objective weighting techniques tend to focus either solely on internal variability (e.g., ENTROPY, SD, SVP, LOPCOW) or exclusively on external structural effects (e.g., MEREC). Although the CRITIC method considers both dimensions, it remains constrained by its dependence on linear associations and parametric assumptions—particularly the assumption of normality. Such methodological limitations can undermine the reliability of decision outcomes and constitute the central motivation of this study. To address this limitation, the study introduces an innovative framework termed the Neyman Chi-Square Distance Approach (NCDA). NCDA is a non-parametric method that simultaneously accounts for both internal variation and external structural divergence. Standard deviation captures internal variability, while Neyman Chi-Square Distance (NCD) quantifies external differences without relying on correlation assumptions. Empirical analyses and case studies reveal that NCDA demonstrates strong robustness against perturbations in the weighting process, as validated through sensitivity analysis. Comparative evaluations confirm the method’s high consistency with well-established approaches, while simulation experiments highlight its capacity to produce balanced and stable weight distributions. Overall, NCDA provides an original and methodologically rigorous contribution to the field by directly integrating distributional distance analysis into the weighting process of decision-making models. This dual-perspective framework not only strengthens the theoretical foundations but also enhances the adaptability and reliability of MCDM applications across complex and heterogeneous data environments.

Anahtar Kelimeler

NCD , Criteria Weight , MCDM , NCDA

Nesnel Kriter Ağırlıklandırması için Yeni Bir Yöntem: Neyman Ki-Kare Uzaklık Yaklaşımı (NCDA)

Öz

Çok kriterli karar verme (MCDM) bağlamında, nesnel ağırlıklandırma tekniklerinin büyük çoğunluğu ya yalnızca içsel değişkenliğe (örneğin ENTROPY, SD, SVP, LOPCOW) ya da bütünüyle dışsal yapısal etkilere (örneğin MEREC) odaklanma eğilimindedir. CRITIC yöntemi her iki boyutu bir araya getirse de, özellikle doğrusal ilişkiler ve parametrik varsayımlar (özellikle normallik varsayımı) konusundaki bağımlılığı nedeniyle kısıtlı kalmaktadır. Bu tür metodolojik sınırlılıklar, karar sonuçlarının güvenilirliğini zayıflatabilmekte ve bu çalışmanın temel motivasyonunu oluşturmaktadır. Bu boşluğu gidermek amacıyla, mevcut araştırma hem içsel varyasyonu hem de dışsal yapısal ayrışmayı aynı anda dikkate alan ve parametrik olmayan bir çerçevede tasarlanan yenilikçi bir yöntem olan Neyman Ki-Kare Uzaklık Yaklaşımı’nı (NCDA) önermektedir. İçsel değişkenlik standart sapma aracılığıyla değerlendirilirken, dışsal dağılımsal farklılıklar korelasyona dayalı varsayımlardan kaçınılarak Neyman Ki-Kare Uzaklık (NCD) metriği üzerinden yakalanmaktadır.

Ampirik analizler, NCDA’nın ağırlıklandırma üzerindeki değişimlere karşı yapılan duyarlılık analizinde güçlü bir sağlamlık sergilediğini göstermektedir. Karşılaştırmalı değerlendirmeler yöntemin yaygın kabul gören yaklaşımlarla tutarlılığını teyit ederken, simülasyon deneyleri ise dengeli ve istikrarlı ağırlık dağılımları üretebilme kapasitesini ortaya koymaktadır. Genel olarak NCDA, dağılımsal uzaklık analizini karar modellerinin ağırlıklandırma sürecine doğrudan entegre ederek alana özgün ve metodolojik açıdan sağlam bir katkı sunmaktadır. Bu çift yönlü bakış açısı yalnızca teorik temelleri güçlendirmekle kalmayıp aynı zamanda karmaşık ve heterojen veri ortamlarında MCDM uygulamalarının uyarlanabilirliğini ve güvenilirliğini de artırmaktadır.

Anahtar Kelimeler

NCD , Kriter ağırlığı , ÇKKV , NCDM

Kaynakça

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