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ExGUtils: A Python Package for Statistical Analysis With the ex-Gaussian Probability Density

机译:ExGUtils:使用前高斯概率密度进行统计分析的Python程序包

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摘要

The study of reaction times and their underlying cognitive processes is an important field in Psychology. Reaction times are often modeled through the ex-Gaussian distribution, because it provides a good fit to multiple empirical data. The complexity of this distribution makes the use of computational tools an essential element. Therefore, there is a strong need for efficient and versatile computational tools for the research in this area. In this manuscript we discuss some mathematical details of the ex-Gaussian distribution and apply the ExGUtils package, a set of functions and numerical tools, programmed for python, developed for numerical analysis of data involving the ex-Gaussian probability density. In order to validate the package, we present an extensive analysis of fits obtained with it, discuss advantages and differences between the least squares and maximum likelihood methods and quantitatively evaluate the goodness of the obtained fits (which is usually an overlooked point in most literature in the area). The analysis done allows one to identify outliers in the empirical datasets and criteriously determine if there is a need for data trimming and at which points it should be done.
机译:反应时间及其潜在的认知过程的研究是心理学的重要领域。反应时间通常通过前高斯分布来建模,因为它可以很好地拟合多个经验数据。这种分布的复杂性使计算工具的使用成为必不可少的要素。因此,强烈需要用于该领域研究的高效且通用的计算工具。在本手稿中,我们讨论了前高斯分布的一些数学细节,并应用了ExGUtils软件包,这是一套针对python编程的函数和数值工具,用于对涉及高斯概率密度的数据进行数值分析。为了验证该包装,我们对使用该包装获得的拟合进行了广泛的分析,讨论了最小二乘和最大似然方法之间的优缺点,并定量评估了所获得拟合的优劣(通常在大多数文献中都被忽略了)区域)。完成的分析使您可以确定经验数据集中的异常值,并仔细确定是否需要数据修整,以及应在何时进行。

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