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Yapay Zeka Hızlı Bakış & Çıkarımlar
AI Insight⚡ TL;DR(1 Cümlede Hızlı Çıkarım)
We find that the least squares method significantly outperforms the maximum likelihood when the sample size is small.
📌 3 Maddede Bu Makale
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Amaç & Hipotez:This paper examines the estimation comparison of two methods for Weibull parameters, one is the maximum likelihood method and the other is the least squares method.
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Temel Bulgular & Veri:A numerical simulation study is carried out to understand performance of the two methods.
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Sonuç & Katkı:We find that the least squares method significantly outperforms the maximum likelihood when the sample size is small.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri
Abstract
This paper examines the estimation comparison of two methods for Weibull parameters, one is the maximum likelihood method and the other is the least squares method. A numerical simulation study is carried out to understand performance of the two methods. Based on sample root mean square errors, we make a comparison between the two computation approaches. We find that the least squares method significantly outperforms the maximum likelihood when the sample size is small.
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Bu Makaleye Atıf Yap
CHU, Y., KE, J. (2012). Computation approaches for parameter estimation of Weibull distribution. Mathematical and Computational Applications, Cilt: 17 - Sayı: 1, 39–47. https://ajindex.com/articles/279839