论文标题
双变量倒数topp-leone模型以对抗异质数据
Bivariate Inverse Topp-Leone Model to Counter Heterogeneous Data
论文作者
论文摘要
在概率和统计数据中，双变量连续特征的可靠建模仍然是一个真正无法克服的考虑因素。在对双变量数据的分析过程中，我们必须处理数据中存在的异质性。因此，为了处理这种情况，我们研究了一项基于Farlie-Gumbel-Morgenstern（FGM）Copula和Inverse Topp-Leone（ITL）模型的新技术。这个想法是使用FGM副群的振荡功能和ITL模型的灵活性，以提出一种严重的双变量解决方案，以建模双变量寿命现象以对抗数据中存在的异质性。理论和实践都是发展的。特别是，我们确定与模型相关的主要函数，例如累积模型函数，概率密度函数，条件密度函数和各种有用的双变量建模依赖性度量。使用Markov Chain Monte Carlo（MCMC）方法的最大似然法和贝叶斯框架估算模型参数。在此之后，使用模型比较方法比较模型。为了解释建议并表明建议使用更好的模型，使用了著名的干旱和毛刺数据集。
In probability and statistics, reliable modeling of bivariate continuous characteristics remains a real insurmountable consideration. During analysis of bivariate data, we have to deal with heterogeneity that is present in data. Therefore, for dealing with such a scenario, we investigate a novel technique based on a Farlie-Gumbel-Morgenstern (FGM) copula and the inverse Topp-Leone (ITL) model in this study. The idea is to use the oscillating functionalities of the FGM copula and the flexibility of the ITL model to propose a serious bivariate solution for the modeling of bivariate lifetime phenomena to counter the heterogeneity present in data. Both theory and practice are developed. In particular, we determine the main functions related to the model, like the cumulative model function, probability density function, conditional density function, and various useful dependence measures for bivariate modeling. The model parameters are estimated using the maximum likelihood method and Bayesian framework of Markov Chain Monte Carlo (MCMC) methodology. Following that, model comparison methods are used to compare models. To explain the findings and show that better models are recommended, the famous Drought and Burr data sets are used.
