Asymptotic theory of parameter estimation by a contrast function based on interpolation error

Yoshihiro Suto, Yan Liu*, Masanobu Taniguchi

*この研究の対応する著者

研究成果: Article査読

2 被引用数 (Scopus)

抄録

Interpolation is an important issue for a variety fields of statistics (e.g., missing data analysis). In time series analysis, the best interpolator for missing points problem has been investigated in several ways. In this paper, the asymptotics of a contrast function estimator defined by pseudo interpolation error for stationary process are investigated. We estimate parameters of the process by minimizing the pseudo interpolation error written in terms of a fitted parametric spectral density and the periodogram based on observed stretch. The estimator has the consistency and asymptotical normality. Although the criterion for the interpolation problem is known as the best in the sense of smallest mean square error for past and future extrapolation, it is shown that the estimator is asymptotically inefficient in general parameter estimation, which leads to an unexpected result.

本文言語English
ページ(範囲)93-110
ページ数18
ジャーナルStatistical Inference for Stochastic Processes
19
1
DOI
出版ステータスPublished - 2016 4 1

ASJC Scopus subject areas

  • 統計学および確率

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