文章基本信息

标题：On Asymptotic Properties and Almost Sure Approximation of the Normalized Inverse-Gaussian Process
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作者：Luai Al Labadi ; Mahmoud Zarepour
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2013
卷号：8
期号：3
页码：553-568
DOI：10.1214/13-BA821
语种：English
出版社：International Society for Bayesian Analysis
摘要：In this paper, similar to the frequentist asymptotic theory, we present large sample theory for the normalized inverse-Gaussian process and its corresponding quantile process. In particular, when the concentration parameter is large, we establish the functional central limit theorem, the strong law of large numbers and the Glivenko-Cantelli theorem for the normalized inverse-Gaussian process and its related quantile process. We also derive a finite sum representation that converges almost surely to the Ferguson and Klass representation of the normalized inverse-Gaussian process. This almost sure approximation can be used to simulate the normalized inverse-Gaussian process.