经验概率生成过程的近似

IF 1.3 Q2 STATISTICS & PROBABILITY
G. Szűcs
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引用次数: 3

摘要

首先,我们完善了r millard和Theodorescu关于经验概率生成过程弱收敛性的论证。然后,我们证明了概率生成过程与相应经验过程之间的一个一般不等式,这很容易暗示一个收敛速度,并简化了弱收敛问题:每当经验过程或其非参数自举版本,或参数估计经验过程或其自举版本收敛时,相应的概率生成过程也会收敛。生成过程的导数也被考虑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Approximations of empirical probability generating processes
Summary First we polish an argument of Rémillard and Theodorescu for the weak convergence of the empirical probability generating process. Then we prove a general inequality between probability generating processes and the corresponding empirical processes, which readily implies a rate of convergence and trivializes the problem of weak convergence: whenever the empirical process or its non-parametric bootstrap version, or the parametric estimated empirical process or its bootstrap version converges, so does the corresponding probability generating process. Derivatives of the generating process are also considered.
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来源期刊
Statistics & Risk Modeling
Statistics & Risk Modeling STATISTICS & PROBABILITY-
CiteScore
1.80
自引率
6.70%
发文量
6
期刊介绍: Statistics & Risk Modeling (STRM) aims at covering modern methods of statistics and probabilistic modeling, and their applications to risk management in finance, insurance and related areas. The journal also welcomes articles related to nonparametric statistical methods and stochastic processes. Papers on innovative applications of statistical modeling and inference in risk management are also encouraged. Topics Statistical analysis for models in finance and insurance Credit-, market- and operational risk models Models for systemic risk Risk management Nonparametric statistical inference Statistical analysis of stochastic processes Stochastics in finance and insurance Decision making under uncertainty.
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