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Journals / Cumhuriyet Science Journal / 2022 / Cilt: 43 - Sayı: 2

A New Extension Form for Continuous Probability Distributions: Uniform-X Distributions

Çiğdem TOPÇU GÜLÖKSÜZ
Nuri ÇELİK
Pages
351–357
Publication Date
2022-10-12
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The new extensions give the best fit to the corresponding data over the well-known probability distributions.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this paper, generating extension forms for continuous probability distribution functions is studied.
📊
Key Findings & Data:The related functions of the new extensions are defined, random samples are generated from the new extensions and the results are presented.
💡
Conclusion & Impact:The new extensions give the best fit to the corresponding data over the well-known probability distributions.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In this paper, generating extension forms for continuous probability distribution functions is studied. The considered transformer function is applied to three well-known probability distributions- Normal, Kumaraswamy, Weibull- and new extensions of these distributions are obtained. The related functions of the new extensions are defined, random samples are generated from the new extensions and the results are presented. Parameter estimation procedures of the extensions are studied, and likelihood equations are obtained. To demonstrate the modeling performance of the extensions, three different data sets are considered, separately. Each data set is modeled by both the corresponding probability distribution and its extensions. The new extensions give the best fit to the corresponding data over the well-known probability distributions.

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Cite This Article

GÜLÖKSÜZ, Ç. T., ÇELİK, N. (2022). A New Extension Form for Continuous Probability Distributions: Uniform-X Distributions. Cumhuriyet Science Journal, Cilt: 43 - Sayı: 2, 351–357. https://ajindex.com/articles/477486

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