统计方法是指有关收集、整理、分析和解释统计数据,并对其所反映的问题作出一定结论的方法。统计方法是一种从微观结构上来研究物质的宏观性质及其规律的独特的方法。

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这本书没有假设读者在统计方面有任何预先训练,这本书的第一部分描述了基本的统计原理,从一个观点,使他们的缺点直观和容易理解。重点是用语言和图形来描述概念。第二部分描述了解决第一部分所涵盖问题的现代方法。使用来自实际研究的数据,包括许多例子来说明传统程序的实际问题,以及更多的现代方法如何能对统计研究的许多领域中得出的结论产生实质性的影响。

这本书的第二版包括了自从第一版出现以来发生的一些进展和见解。包括与中位数相关的新结果,回归,关联的测量,比较依赖组的策略,处理异方差的方法,以及效应量的测量。

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In this paper we consider a Gaussian mixture model where the mixture weight behaves as an unknown function of time. To estimate the mixture weight function, we develop a Bayesian nonlinear dynamic approach for polynomial models. Two estimation methods that can be extended to other situations are considered. One of them, called here component-wise Metropolis-Hastings, is more general and can be used for any situation where the observation and state equations are nonlinearly connected. The other method tends to be faster but must be applied specifically to binary data (by using a probit link function). This kind of Gaussian mixture model is capable of successfully capturing the features of the data, as observed in numerical studies. It can be useful in studies such as clustering, change-point and process control. We apply the proposed method an array Comparative Genomic Hybridization (aCGH) dataset from glioblastoma cancer studies, where we illustrate the ability of the new method to detect chromosome aberrations.

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In this paper we consider a Gaussian mixture model where the mixture weight behaves as an unknown function of time. To estimate the mixture weight function, we develop a Bayesian nonlinear dynamic approach for polynomial models. Two estimation methods that can be extended to other situations are considered. One of them, called here component-wise Metropolis-Hastings, is more general and can be used for any situation where the observation and state equations are nonlinearly connected. The other method tends to be faster but must be applied specifically to binary data (by using a probit link function). This kind of Gaussian mixture model is capable of successfully capturing the features of the data, as observed in numerical studies. It can be useful in studies such as clustering, change-point and process control. We apply the proposed method an array Comparative Genomic Hybridization (aCGH) dataset from glioblastoma cancer studies, where we illustrate the ability of the new method to detect chromosome aberrations.

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