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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/139849

Title:	Evaluating reliability of tree-patterns in extreme-K categorical samples problems
Authors:	周珮婷 Chou, Elizabeth Hsieh, Yin-Chen Enriquez, Sabrina Hsieh, Fushing
Contributors:	統計系
Keywords:	Extreme-K;exploratory data analysis;hierarchical clustering;ANOVA
Date:	2021-07
Issue Date:	2022-04-12
Abstract:	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.
Relation:	Journal of Statistical Computation and Simulation, Vol.91, No.18, pp.3828-3849
Data Type:	article
DOI 連結:	https://doi.org/10.1080/00949655.2021.1951266
DOI:	10.1080/00949655.2021.1951266
Appears in Collections:	[統計學系] 期刊論文

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