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  • 标题:On Asymptotic Properties and Almost Sure Approximation of the Normalized Inverse-Gaussian Process
  • 本地全文:下载
  • 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.
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