Non-asymptotic Confidence Estimation of the Autoregressive Parameter in AR(1) Process with an Unknown Noise Variance

Authors

  • Sergey E. Vorobeychikov Tomsk State University
  • Yulia B. Burkatovskaya Tomsk State University, Tomsk Polytechnic University

DOI:

https://doi.org/10.17713/ajs.v49i4.1121

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 unknown. 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.

Published

2020-04-13

How to Cite

Vorobeychikov, S. E., & Burkatovskaya, Y. B. (2020). Non-asymptotic Confidence Estimation of the Autoregressive Parameter in AR(1) Process with an Unknown Noise Variance. Austrian Journal of Statistics, 49(4), 19-26. https://doi.org/10.17713/ajs.v49i4.1121

Issue

Section

Special Issue CDAM conference