Generalized Bernoulli process: simulation, estimation, and application

IF 0.6 Q4 STATISTICS & PROBABILITY

Dependence Modeling Pub Date : 2021-01-01 DOI:10.1515/demo-2021-0106

Jeonghwa Lee

引用次数: 3

Abstract

Abstract A generalized Bernoulli process (GBP) is a stationary process consisting of binary variables that can capture long-memory property. In this paper, we propose a simulation method for a sample path of GBP and an estimation method for the parameters in GBP. Method of moments estimation and maximum likelihood estimation are compared through empirical results from simulation. Application of GBP in earthquake data during the years of 1800-2020 in the region of conterminous U.S. is provided.

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广义伯努利过程:模拟、估计及应用

广义伯努利过程(GBP)是由具有长记忆特性的二元变量组成的平稳过程。在本文中，我们提出了一种GBP样本路径的仿真方法和GBP中参数的估计方法。通过仿真的经验结果对矩估计方法和极大似然估计方法进行了比较。给出了GBP在1800-2020年美国邻近地区地震资料中的应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Dependence Modeling STATISTICS & PROBABILITY-

CiteScore

1.00

自引率

0.00%

发文量

审稿时长

12 weeks

期刊介绍： The journal Dependence Modeling aims at providing a medium for exchanging results and ideas in the area of multivariate dependence modeling. It is an open access fully peer-reviewed journal providing the readers with free, instant, and permanent access to all content worldwide. Dependence Modeling is listed by Web of Science (Emerging Sources Citation Index), Scopus, MathSciNet and Zentralblatt Math. The journal presents different types of articles: -"Research Articles" on fundamental theoretical aspects, as well as on significant applications in science, engineering, economics, finance, insurance and other fields. -"Review Articles" which present the existing literature on the specific topic from new perspectives. -"Interview articles" limited to two papers per year, covering interviews with milestone personalities in the field of Dependence Modeling. The journal topics include (but are not limited to):　 -Copula methods -Multivariate distributions -Estimation and goodness-of-fit tests -Measures of association -Quantitative risk management -Risk measures and stochastic orders -Time series -Environmental sciences -Computational methods and software -Extreme-value theory -Limit laws -Mass Transportations