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  • 标题:Recovering Fisher-Information from the MGF Alone without Requiring Explicit PMF or PDF from a One-Parameter Exponential Family
  • 本地全文:下载
  • 作者:Nitis Mukhopadhyay
  • 期刊名称:Sri Lankan Journal of Applied Statistics
  • 印刷版ISSN:1391-4987
  • 电子版ISSN:2424-6271
  • 出版年度:2018
  • 卷号:19
  • 期号:1
  • 页码:1-12
  • DOI:10.4038/sljastats.v19i1.7984
  • 出版社:The Institute of Applied Statistics, Sri Lanka
  • 摘要:It is well-known that a finite moment generating function (m.g.f.) corresponds to a unique probability distribution. So, an important question arises: Is it possible to obtain an expression of Fisher-information, IX(Ɵ) ; using the m.g.f. alone, that is without requiring explicitly a probability mass function (p.m.f.) or probability density function (p.d.f.), given that the p.m.f. or p.d.f came from a one-parameter exponential family? We revisit the core of statistical inference by developing a clear link (Theorem 1.1) between the m.g.f. and IX(Ɵ) . Illustrations are included.
  • 关键词:Cram_er-Rao inequality; Cram_er-Rao lower bound; Exponential family; Fisher-information; Minimal su_cient statistic; Moment generating function; One-parameter exponential
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