Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/93884
標題: Good-Turing frequency estimation in a finite population
作者: Hwang, Wen-Han
Lin, Chih-Wei
Shen, Tsung-Jen
關鍵字: Finite population;Frequency estimation;Good-Turing;Number-of-classes estimation;Sample coverage;Shannon index;Plants;Probability;Sample Size;Statistics as Topic
Project: Biometrical journal. Biometrische Zeitschrift, Volume 57, Issue 2, Page(s) 321-39.
摘要: 
Good-Turing frequency estimation (Good, ) is a simple, effective method for predicting detection probabilities of objects of both observed and unobserved classes based on observed frequencies of classes in a sample. The method has been used widely in several disciplines, such as information retrieval, computational linguistics, text recognition, and ecological diversity estimation. Nevertheless, existing studies assume sampling with replacement or sampling from an infinite population, which might be inappropriate for many practical applications. In light of this limitation, this article presents a modification of the Good-Turing estimation method to account for finite population sampling. We provide three practical extensions of the modified method, and we examine performance of the modified method and its extensions in simulation experiments.
URI: http://hdl.handle.net/11455/93884
DOI: 10.1002/bimj.201300168
Appears in Collections:統計學研究所

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