Nonparametric Goodness-of-Fit Testing Under Gaussian Models
Nonparametric Goodness-of-Fit Testing Under Gaussian Models
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DOI：
10.1198/jasa.2004.s331
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发表时间：
2004-06
影响因子：
3.7
通讯作者：
C. Pouet
中科院分区：
文献类型：
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作者：
C. Pouet
最近的发展从假设检验转向模型选择和最大似然、贝叶斯和参数的近似贝叶斯推理。尽管这些主题对于群体遗传学来说相当新，但希望回答序言中提出的问题的实证研究人员——“给定一组 DNA 序列，什么潜在的力量导致了观察到的变异模式？”——将会有兴趣了解它们。最后，尽管可以有效地使用示例来描述各个方法，但将多种技术连接到同一示例数据集的某种综合将有助于经验群体遗传学家设计他们自己的数据分析。尽管有这些限制，如果与在更广泛的生物学背景下陈述结果的文本结合使用，本书将对那些具有数学或统计背景但第一次接触该主题的人有所帮助。这本书比该领域的大多数书籍更具理论性，并且由于它比相关经典著作（例如 Crow 和 Kimura（1970）以及 Ewens（1979）的著作）更现代，因此它将成为该领域研究人员的参考资料。
recent developments that move away from hypothesis tests toward model selection and maximum likelihood, Bayesian, and approximate Bayesian inference of parameters are absent. Although these topics are fairly new to population genetics, empirical investigators who wish to answer the question posed in the Preface—“given a collection of DNA sequences, what underlying forces are responsible for the observed patterns of variability?”—would be interested to know about them. Finally, although examples are used effectively to describe individual methods, some synthesis that connects several techniques to the same example dataset would help empirical population geneticists design analyses of their own data. Despite these limitations, if used in conjunction with texts that state results in broader biological context, this book would be helpful to those with a mathematical or statistical background who are encountering the subject for the rst time. The book is more theoretical than most books in the area, and because it is more modern than related classics, such as those by Crow and Kimura (1970) and Ewens (1979), it will be an informative reference for researchers in the eld.