Non-asymptotic confidence estimation of the autoregressive parameter in AR(1) process with an unknown noise variance

Sergey E. Vorobeychikov, Yulia B. Burkatovskaya

Research output: Contribution to journalArticle

Abstract

The paper considers the estimation problem of the autoregressive parameter in the first-order autoregressive process with Gaussian noises when the noise variance is un-known. We propose a non-asymptotic technique to compensate the unknown variance, and then, to construct a point estimator with any prescribed mean square accuracy. Also a fixed-width confidence interval with any prescribed coverage accuracy is proposed. The results of Monte-Carlo simulations are given.

Original languageEnglish
Pages (from-to)19-26
Number of pages8
JournalAustrian Journal of Statistics
Volume49
Issue number4
DOIs
Publication statusPublished - 14 Apr 2020

Keywords

  • Autoregressive process
  • Confidence interval
  • Non-asymptotic estimation

ASJC Scopus subject areas

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

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