论文标题
极性总正态分布
The polar-generalized normal distribution
论文作者
论文摘要
本文通过极性方法引入了正态分布的扩展，以捕获双峰性和不对称性，这通常是经验数据的特征。后来的两个功能完全由单独的标量参数控制。累积分布函数，密度函数和力矩的显式表达式。分布的随机表示有助于通过马尔可夫链蒙特卡洛方法实施贝叶斯估计。分析了一些现实生活中的数据以及模拟数据，以说明用于建模非对称双峰性的分布的灵活性。
This paper introduces an extension to the normal distribution through the polar method to capture bimodality and asymmetry, which are often observed characteristics of empirical data. The later two features are entirely controlled by a separate scalar parameter. Explicit expressions for the cumulative distribution function, the density function and the moments were derived. The stochastic representation of the distribution facilitates implementing Bayesian estimation via the Markov chain Monte Carlo methods. Some real-life data as well as simulated data are analyzed to illustrate the flexibility of the distribution for modeling asymmetric bimodality.
