On a Transform for Modeling Skewness

L. Kang, P. Damien, S. Walker
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引用次数: 3

Abstract

In many applications, data exhibit skewness and in this paper we present a new family of density functions modeling skewness based on a transformation, analagous to those of location and scale. Here we note that location will always refer to mode. Hence, in order to model data to include shape, we need only to find a family of densities exhibiting a variety of shapes, since we can obtain the other three properties via the transformations. The chosen class of densities with the variety of shape is, we argue, the simplest available. Illustrations including regression and time series models are given.
关于偏度建模的变换
在许多应用中,数据表现出偏性,在本文中,我们提出了一种新的密度函数族,它基于一种类似于位置和尺度的转换来建模偏性。这里我们注意到location总是指向mode。因此,为了对包含形状的数据进行建模,我们只需要找到一系列表现出各种形状的密度,因为我们可以通过转换获得其他三个属性。我们认为,所选择的具有各种形状的密度类别是最简单的。给出了包括回归模型和时间序列模型在内的实例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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