| dc.contributor | 統計系 | |
| dc.creator (作者) | 周珮婷 | |
| dc.creator (作者) | Chou, Elizabeth | |
| dc.creator (作者) | Hsieh, Yin-Chen | |
| dc.creator (作者) | Enriquez, Sabrina | |
| dc.creator (作者) | Hsieh, Fushing | |
| dc.date (日期) | 2021-07 | |
| dc.date.accessioned | 2022-04-12 | - |
| dc.date.available | 2022-04-12 | - |
| dc.date.issued (上傳時間) | 2022-04-12 | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/139849 | - |
| dc.description.abstract (摘要) | Exploratory Data Analysis (EDA) approaches are adopted to address the difficult extreme-K categorical sample problem. Due to observed data`s categorical nature, all comparisons among populations are performed by comparing their distributions in the form of a histogram with symbolic bins. A distance measure is designed to evaluate the discrepancy between two symbol-based histograms to facilitate Hierarchical Clustering (HC) algorithms. The resultant binary HC-tree then serves as the basis for our EDA task of discovering tree-patterns of interest. Since each population-leaf`s location within a binary HC-tree`s geometry is expressed through a binary code sequence, a binary code segment characterizes all commonly shared tree-patterns for all members. We then generate a large ensemble of mimicries of the observed dataset based on multinomial distributions and construct a large ensemble of binary HC-trees. Upon each identified tree-pattern which we determined based on the observed dataset, we evaluate its reliability and uncertainty through two histograms. | |
| dc.format.extent | 148 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Journal of Statistical Computation and Simulation, Vol.91, No.18, pp.3828-3849 | |
| dc.subject (關鍵詞) | Extreme-K;exploratory data analysis;hierarchical clustering;ANOVA | |
| dc.title (題名) | Evaluating reliability of tree-patterns in extreme-K categorical samples problems | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1080/00949655.2021.1951266 | |
| dc.doi.uri (DOI) | https://doi.org/10.1080/00949655.2021.1951266 | |
