Non-asymptotic Confidence Estimation of the Autoregressive Parameter in AR(1) Process with an Unknown Noise Variance
| Parent link: | Austrian Journal of Statistics Vol. 49, № 4 : Special Issue CDAM 2019.— 2020.— [P. 19-26] |
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| Gaia: | Title screen 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. |
| Hizkuntza: | ingelesa |
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2020
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| Sarrera elektronikoa: | https://doi.org/10.17713/ajs.v49i4.1121 |
| Formatua: | Baliabide elektronikoa Liburu kapitulua |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=663397 |