论文标题

分散模型的构建和扩展

Construction and Extension of Dispersion Models

论文作者

Labouriau, Rodrigo

论文摘要

文献中研究了两种主要的分散模型:正确(PDM)和指数分散模型(EDM)。在这里,既不适当也不是指数性分散模型的分散模型在这里称为非标准色散模型(NSDM)。本文揭示了一种构建新PDM和NSDM的技术。该构建为分散理论模型中的一个关于非标准分散模型的扩展提供了解决方案。给定单位偏差函数,通常通过计算使密度函数集成一个函数的归一化函数来构建分散模型。该计算涉及非平地积分方程的解决方案。这里探讨的主要思想是使用实际非晶格对称概率度量的特征功能来构建一个足够规律的单位偏差系列,以使相关的积分方程可拖动。与这些单位偏差相关的积分方程允许一个微不足道的解决方案,因为正常化函数是独立于观察值的恒定函数。但是,我们使用分布机制(即广义函数)和相对于特殊构建的RIEZ系统的扩展,这些积分方程还可以无限地接受许多非平凡的解决方案,从而产生许多NSDM。我们得出的结论是,非标准色散模型类的基数大于实际非晶格对称概率测度的基数。

There are two main classes of dispersion models studied in the literature: proper (PDM), and exponential dispersion models (EDM). Dispersion models that are neither proper nor exponential dispersion models are termed here non-standard dispersion models (NSDM). This paper exposes a technique for constructing new PDMs and NSDMs. This construction provides a solution to an open question in the theory of dispersion models about the extension of non-standard dispersion models. Given a unit deviance function, a dispersion model is usually constructed by calculating a normalising function that makes the density function integrates one. This calculation involves the solution of non-trivial integral equations. The main idea explored here is to use characteristic functions of real non-lattice symmetric probability measures to construct a family of unit deviances that are sufficiently regular to make the associated integral equations tractable. The integral equations associated to those unit deviances admit a trivial solution, in the sense that the normalising function is a constant function independent of the observed values. However, we show, using the machinery of distributions (i.e., generalised functions) and expansions of the normalising function with respect to specially constructed Riez systems, that those integral equations also admit infinitely many non-trivial solutions, generating many NSDMs. We conclude that, the cardinality of the class of non-standard dispersion models is larger than the cardinality of the class of real non-lattice symmetric probability measures.

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