The Continuous Bernoulli Distribution: Mathematical Characterization, Fractile Regression, Computational Simulations, and Applications

被引:4
|
作者
Korkmaz, Mustafa C. [1 ]
Leiva, Victor [2 ]
Martin-Barreiro, Carlos [3 ,4 ]
机构
[1] Artvin Coruh Univ, Dept Measurement & Evaluat, TR-08100 Artvin, Turkiye
[2] Pontificia Univ Catolica Valparaiso, Sch Ind Engn, Valparaiso 2362807, Chile
[3] Escuela Super Politecn Litoral ESPOL, Fac Nat Sci & Math, Guayaquil 090902, Ecuador
[4] Univ Espiritu Santo, Fac Engn, Samborondon 0901952, Ecuador
关键词
Bernoulli distribution; likelihood and Monte Carlo methods; point estimation; quantile function; R software; residual analysis; LINDLEY DISTRIBUTION; BETA REGRESSION; MODEL;
D O I
10.3390/fractalfract7050386
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
The continuous Bernoulli distribution is defined on the unit interval and has a unique property related to fractiles. A fractile is a position on a probability density function where the corresponding surface is a fixed proportion. This article presents the derivation of properties of the continuous Bernoulli distribution and formulates a fractile or quantile regression model for a unit response using the exponentiated continuous Bernoulli distribution. Monte Carlo simulation studies evaluate the performance of point and interval estimators for both the continuous Bernoulli distribution and the fractile regression model. Real-world datasets from science and education are analyzed to illustrate the modeling abilities of the continuous Bernoulli distribution and the exponentiated continuous Bernoulli quantile regression model.
引用
收藏
页数:22
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