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具有医学数据应用的离散-连续双变量分布新模型。

A New Model of Discrete-Continuous Bivariate Distribution with Applications to Medical Data.

机构信息

Department of Mathematics, Faculty of Science, Jeddah University, 2749 Asfan Rd. Jeddah 21589, Saudi Arabia.

Department of Mathematics, Faculty of Science, Al-Azhar University, Nasr city, Cairo, Egypt.

出版信息

Comput Math Methods Med. 2022 May 21;2022:1883491. doi: 10.1155/2022/1883491. eCollection 2022.

Abstract

The bivariate Poisson exponential-exponential distribution is an important lifetime distribution in medical data analysis. In this article, the conditionals, probability mass function (pmf), Poisson exponential and probability density function (pdf), and exponential distribution are used for creating bivariate distribution which is called bivariate Poisson exponential-exponential conditional (BPEEC) distribution. Some properties of the BPEEC model are obtained such as the normalized constant, conditional densities, regression functions, and product moment. Moreover, the maximum likelihood and pseudolikelihood methods are used to estimate the BPEEC parameters based on complete data. Finally, two data sets of real bivariate data are analyzed to compare the methods of estimation. In addition, a comparison between the BPEEC model with the bivariate exponential conditionals (BEC) and bivariate Poisson exponential conditionals (BPEC) is considered.

摘要

双变量泊松指数-指数分布是医学数据分析中一种重要的寿命分布。在本文中,使用条件、概率质量函数(pmf)、泊松指数和概率密度函数(pdf)以及指数分布来创建双变量分布,称为双变量泊松指数-指数条件(BPEEC)分布。获得了 BPEEC 模型的一些性质,例如归一化常数、条件密度、回归函数和乘积矩。此外,基于完整数据,使用最大似然和拟似然方法来估计 BPEEC 参数。最后,分析了两个真实的双变量数据集,以比较估计方法。此外,还考虑了 BPEEC 模型与双变量指数条件(BEC)和双变量泊松指数条件(BPEC)之间的比较。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe06/9148234/2b40e7bf0517/CMMM2022-1883491.001.jpg

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