On sequential confidence interval in a stationary Gaussian process

IF 0.6 4区数学 Q4 STATISTICS & PROBABILITY

Sequential Analysis-Design Methods and Applications Pub Date : 2021-10-02 DOI:10.1080/07474946.2021.2010414

Pritam Sarkar, U. Bandyopadhyay

引用次数: 0

Abstract

Abstract In this article we concentrate on fixed accuracy intervals of the common variance when the data arise from a Gaussian process with order 1 autoregressive covariance structure. Our approach includes the maximum likelihood method and least squares method for estimating the parameters in this process. We provide necessary asymptotic results and carry out numerical evaluations.

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平稳高斯过程的序贯置信区间

摘要在本文中，当数据来自具有1阶自回归协方差结构的高斯过程时，我们集中于公共方差的固定精度区间。我们的方法包括最大似然法和最小二乘法来估计这个过程中的参数。我们提供了必要的渐近结果，并进行了数值评估。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Sequential Analysis-Design Methods and Applications STATISTICS & PROBABILITY-

CiteScore

1.40

自引率

12.50%

发文量

期刊介绍： The purpose of Sequential Analysis is to contribute to theoretical and applied aspects of sequential methodologies in all areas of statistical science. Published papers highlight the development of new and important sequential approaches. Interdisciplinary articles that emphasize the methodology of practical value to applied researchers and statistical consultants are highly encouraged. Papers that cover contemporary areas of applications including animal abundance, bioequivalence, communication science, computer simulations, data mining, directional data, disease mapping, environmental sampling, genome, imaging, microarrays, networking, parallel processing, pest management, sonar detection, spatial statistics, tracking, and engineering are deemed especially important. Of particular value are expository review articles that critically synthesize broad-based statistical issues. Papers on case-studies are also considered. All papers are refereed.