ROBUST REGRESSION AND INTERPOLATION FOR TIME SERIES

研究成果: Article査読

11 被引用数 (Scopus)

抄録

Abstract. In this paper we shall consider the interpolation problem under the condition that the spectral density of a stationary process concerned is vaguely known (i.e., Huber's ε ‐contaminated model). Then we can get a minimax robust interpolator for the class of spectral densities S={ g:g(x)=(1‐ε)f(x)+εh(x)ε Ar Do, 0<ε<1}, where f(x) is a known spectral density and D0 is a certain class of spectral densities. Also we shall consider the time series regression problem under the condition that the residual spectral density is vaguely known. Then we can get a minimax robust regression coefficient estimate for the class of the residual spectral densities S.

本文言語English
ページ(範囲)53-62
ページ数10
ジャーナルJournal of Time Series Analysis
2
1
DOI
出版ステータスPublished - 1981
外部発表はい

ASJC Scopus subject areas

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

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