Non-asymptotic confidence estimation of the autoregressive parameter in AR(1) process with an unknown noise variance S. E. Vorobeychikov, Y. B. Burkatovskaya
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ArticleContent type: Текст Media type: электронный Subject(s): авторегрессионный процесс | доверительный интервал | авторегрессияGenre/Form: статьи в журналах Online resources: Click here to access online
In:
Austrian journal of statistics Vol. 49, № 4. P. 19-26Abstract: The paper considers the estimation problem of the autoregressive parameter in the
rst-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 xed-width condence interval with any prescribed coverage accuracy is proposed. The
results of Monte-Carlo simulations are given.
Библиогр.: c. 25-26
The paper considers the estimation problem of the autoregressive parameter in the
rst-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 xed-width condence interval with any prescribed coverage accuracy is proposed. The
results of Monte-Carlo simulations are given.
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