Specifically, we will concentrate on the symmetric family of distibutions and skew distributions (Weibull and Generalized Logistic).
📌 3 Key Takeaways
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Objective & Hypothesis:The aim of this study is to show that least squares estimators (LS) used commonly in application problems are not efficient estimators for the nonnormal distributions.
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Key Findings & Data:The aim of this study is to show that least squares estimators (LS) used commonly in application problems are not efficient estimators for the nonnormal distributions.
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Conclusion & Impact:Specifically, we will concentrate on the symmetric family of distibutions and skew distributions (Weibull and Generalized Logistic).
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
The aim of this study is to show that least squares estimators (LS) used commonly in application problems are not efficient estimators for the nonnormal distributions. Specifically, we will concentrate on the symmetric family of distibutions and skew distributions (Weibull and Generalized Logistic).
Özet
Bu çalışmanın temel amacı uygulamada yaygın olarak kullanılan En Küçük Kareler (EKK) Kesimcilerinin Normal dağılım dışındaki dağılımlar için etkin kestiriciler olmadığının gösterilmesidir. Özellikle normal olmayan simetrik dağılımlar ailesi ve çarpık dağılımlar (Weibull ve genelleştirilmiş Lojistik) üzerinde durulacaktır.
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Cite This Article
ÖZARICI, Ö., ŞENOĞLU, B., FİDAN, H. (2022). Investigating the efficiencies of least squares estimators for some nonnormal distributions. Anadolu Üniversitesi Bilim ve Teknoloji Dergisi :A-Uygulamalı Bilimler ve Mühendislik, Cilt: 2 - Sayı: 2, 369–373. https://ajindex.com/articles/438525
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