高师理科学刊2023，Vol.43Issue(12)：15-21,7.DOI:10.3969/j.issn.1007-9831.2023.12.003
对数正态分布序列单均值变点的识别和估计
Identification and estimation of single mean change points in lognormal distribution sequences
摘要
Abstract
In response to the situation where the data presents a skewed distribution and there are change points,a single mean change point model with a lognormal distribution is constructed.The likelihood function of the mean single change point model for this distribution is given,and the maximum likelihood method and Bayesian method are used to identify and estimate the position of the change points.Through simulation and comparative research,both methods can effectively estimate the position of the change point.Under the criteria of standard deviation and relative error,the Bayesian method is more effective than the maximum likelihood method.The Bayesian method under conjugate prior distribution performs better in identifying and estimating the position of change points compared to the Bayesian method without prior information.关键词
单均值变点/对数正态分布/贝叶斯方法/极大似然方法Key words
single mean change point/lognormal distribution/Bayesian method/maximum likelihood method分类
数学引用本文复制引用
陈丽曲,黄介武..对数正态分布序列单均值变点的识别和估计[J].高师理科学刊,2023,43(12):15-21,7.基金项目
贵州省科技计划基金项目(黔科合基础[2017]1083号) （黔科合基础[2017]1083号）
贵州省教育厅自然科学研究项目(黔教技[2022]015号) （黔教技[2022]015号）
