LAN theorem for non-Gaussian locally stationary processes and its applications

Junichi Hirukawa, Masanobu Taniguchi

研究成果: Article査読

6 被引用数 (Scopus)

抄録

For a class of locally stationary processes introduced by Dahlhaus, we derive the LAN theorem under non-Gaussianity and apply the results to asymptotically optimal estimation and testing problems. For a class F of statistics which includes important statistics, we derive the asymptotic distributions of statistics in F under contiguous alternatives of unknown parameter. Because the asymptotics depend on the non-Gaussianity of the process, we discuss the non-Gaussian robustness. An interesting feature of effect of non-Gaussianity is elucidated in terms of LAN. Furthermore, the LAN theorem is applied to adaptive estimation when the innovation density is unknown.

本文言語English
ページ(範囲)640-688
ページ数49
ジャーナルJournal of Statistical Planning and Inference
136
3
DOI
出版ステータスPublished - 2006 3 1

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

フィンガープリント 「LAN theorem for non-Gaussian locally stationary processes and its applications」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル