介绍Bent Jørgensen的想法

G. Cordeiro, R. Labouriau, D. Botter
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引用次数: 5

摘要

我们简要地揭示了色散模型理论和应用的一些关键方面,其中Bent Jorgensen作为驱动力和灵感来源发挥了至关重要的作用。从分散模型的一般概念开始,使用极简主义的数学假设,我们专注于两类具有不同统计风格的分布:指数分散和适当分散模型。色散模型的构建涉及到积分方程的求解,这些方程通常是难以处理的。当假设一个更数学化的结构时,这些困难就消失了:它简化为分别计算指数色散和适当色散模型的力矩生成函数或Riemann-Stieltjes积分。介绍了一种基于特征函数构造色散模型的新方法,将上述积分方程转化为易于处理的卷积方程,并给出了既不是固有色散模型也不是指数色散模型的色散模型的实例。一个推论是规则和非规则色散模型的基数都很大。讨论了一些选定的应用,包括指数族非线性模型(其中广义线性模型是特殊情况)和基于潜在Levy过程的聚类和依赖数据的几种模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An introduction to Bent Jørgensen’s ideas
We briefly expose some key aspects of the theory and use of dispersion models, for which Bent Jorgensen played a crucial role as a driving force and an inspiration source. Starting with the general notion of dispersion models, built using minimalistic mathematical assumptions, we specialize in two classes of families of distributions with different statistical flavors: exponential dispersion and proper dispersion models. The construction of dispersion models involves the solution of integral equations that are, in general, untractable. These difficulties disappear when a more mathematical structure is assumed: it reduces to the calculation of a moment generating function or of a Riemann-Stieltjes integral for the exponential dispersion and the proper dispersion models, respectively. A new technique for constructing dispersion models based on characteristic functions is introduced turning the integral equations above into a tractable convolution equation and yielding examples of dispersion models that are neither proper dispersion nor exponential dispersion models. A corollary is that the cardinality of regular and non-regular dispersion models are both large. Some selected applications are discussed including exponential families non-linear models (for which generalized linear models are particular cases) and several models for clustered and dependent data based on a latent Levy process.
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