Asymptotic analysis of an algorithm for identification of quantized AR time-series

V. Krishnamurthy, H. Poor
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引用次数: 1

Abstract

Krishnamurthy and Mareels presented a parameter estimation algorithm called the binary series estimation algorithm (BSEA) for Gaussian auto-regressive (AR) time series given 1-bit quantized noisy measurements. The present authors carry out an asymptotic analysis of the BSEA for Gaussian AR models. In particular, from a central limit theorem they obtain expressions for the asymptotic covariances of the parameter estimates. From this they: (1) Present an algorithm for estimating the order of an AR series from one-bit quantized measurements. (2) Theoretically they justify why BSEA can yield better estimates than the Yule-Walker methods in some cases.
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一种量化AR时间序列识别算法的渐近分析
Krishnamurthy和Mareels针对给定1比特量化噪声的高斯自回归(AR)时间序列,提出了一种参数估计算法——二值序列估计算法(BSEA)。本文作者对高斯AR模型的BSEA进行了渐近分析。特别地,他们从中心极限定理得到了参数估计的渐近协方差的表达式。在此基础上,他们:(1)提出了一种从一比特量化测量中估计AR序列阶数的算法。(2)从理论上讲,他们证明了为什么在某些情况下BSEA可以比Yule-Walker方法产生更好的估计。
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