The Negative Binomial Multiple Change Point Algorithm is a hybrid change detection and estimation approach that works well for overdispersed and equidispersed count data. This simulation study assesses the performance...The Negative Binomial Multiple Change Point Algorithm is a hybrid change detection and estimation approach that works well for overdispersed and equidispersed count data. This simulation study assesses the performance of the NBMCPA under varying sample sizes and locations of true change points. Various performance metrics are calculated based on the change point estimates and used to assess how well the model correctly identifies change points. Errors in estimation of change points are obtained as absolute deviations of known change points from the change points estimated under the algorithm. Algorithm robustness is evaluated through error analysis and visualization techniques including kernel density estimation and computation of metrics such as change point location accuracy, precision, sensitivity and false positive rate. The results show that the model consistently detects change points that are present and does not erroneously detect changes where there are none. Change point location accuracy and precision of the NBMCPA increases with sample size, with best results for medium and large samples. Further model accuracy and precision are highest for changes located in the middle of the dataset compared to changes located in the periphery.展开更多
文章主要研究了安徽省某气象台站年平均气温、月平均气温的结构性变化情况。该文在对气温数据进行正态性检验的基础上,运用ASAMC(annealing stochastic approximation Monte Carlo)算法对气温数据进行统计分析,估计出平均气温结构性变...文章主要研究了安徽省某气象台站年平均气温、月平均气温的结构性变化情况。该文在对气温数据进行正态性检验的基础上,运用ASAMC(annealing stochastic approximation Monte Carlo)算法对气温数据进行统计分析,估计出平均气温结构性变化的位置,并探索发生结构性变化的气象因素和非气象因素。展开更多
文摘The Negative Binomial Multiple Change Point Algorithm is a hybrid change detection and estimation approach that works well for overdispersed and equidispersed count data. This simulation study assesses the performance of the NBMCPA under varying sample sizes and locations of true change points. Various performance metrics are calculated based on the change point estimates and used to assess how well the model correctly identifies change points. Errors in estimation of change points are obtained as absolute deviations of known change points from the change points estimated under the algorithm. Algorithm robustness is evaluated through error analysis and visualization techniques including kernel density estimation and computation of metrics such as change point location accuracy, precision, sensitivity and false positive rate. The results show that the model consistently detects change points that are present and does not erroneously detect changes where there are none. Change point location accuracy and precision of the NBMCPA increases with sample size, with best results for medium and large samples. Further model accuracy and precision are highest for changes located in the middle of the dataset compared to changes located in the periphery.
文摘文章主要研究了安徽省某气象台站年平均气温、月平均气温的结构性变化情况。该文在对气温数据进行正态性检验的基础上,运用ASAMC(annealing stochastic approximation Monte Carlo)算法对气温数据进行统计分析,估计出平均气温结构性变化的位置,并探索发生结构性变化的气象因素和非气象因素。
