一类新的有界支持上分布及其回归模型

Q1 Decision Sciences
Ishfaq S. Ahmad, Rameesa Jan, Poonam Nirwan, Peer Bilal Ahmad
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引用次数: 0

摘要

本文介绍并详细研究了有界支持(0,1)上的一种新的双参数分布。本文讨论了一些有趣的统计特性,如凹性、危险率函数、平均残差寿命、矩和量子函数。矩法和最大似然估计方法用于估计所提模型的未知参数。此外,还通过蒙特卡洛模拟研究评估了估计方法的有限样本性能。与许多已知的单位区间双参数分布相比,将提出的分布应用于实际数据集显示出更好的拟合效果。此外,还引入了一个新的回归模型,作为各种单位区间回归模型的替代。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A New Class of Distribution Over Bounded Support and Its Associated Regression Model

A New Class of Distribution Over Bounded Support and Its Associated Regression Model

In this paper, a new two-parameter distribution over the bounded support (0,1) is introduced and studied in detail. Some of the interesting statistical properties like concavity, hazard rate function, mean residual life, moments and quantile function are discussed. The method of moments and maximum likelihood estimation methods are used to estimate unknown parameters of the proposed model. Besides, finite sample performance of estimation methods are evaluated through the Monte-Carlo simulation study. Application of the proposed distribution to the real data sets shows a better fit than many known two-parameter distributions on the unit interval. Moreover, a new regression model as an alternative to various unit interval regression models is introduced.

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来源期刊
Annals of Data Science
Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
6.50
自引率
0.00%
发文量
93
期刊介绍: Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed.     ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
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