Non-asymptotic Confidence Estimation of the Autoregressive Parameter in AR(1) Process with an Unknown Noise Variance
Sergey E. Vorobeychikov, Yulia B. Burkatovskaya
Austrian Journal of Statistics · 2020
doi:10.17713/ajs.v49i4.1121 (opens in a new tab)
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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.
Abstract licence: CC BY
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