Methods for estimating the autocorrelation and power spectral density functions when there are many missing data values

N. Grossbard, E. Dewan
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引用次数: 5

Abstract

A new method for estimating the autocorrelation and the crosscorrelation has been developed. The resulting estimates are usually more accurate than the classical values. The method is particularly useful when there are many missing data values. For the case when there are many missing data values, it is suggested that a power spectral density (PSD) of the autocorrelation function can be developed. The resulting PSD can easily be mapped into the PSD of the original data. Towards this end, Burg's technique has been applied to the autocorrelation and the results of the application are presented.<>
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缺失数据多时自相关函数和功率谱密度函数的估计方法
提出了一种新的自相关和互相关估计方法。结果估计通常比经典值更准确。当存在许多缺失数据值时,该方法特别有用。对于缺失数据较多的情况,建议建立自相关函数的功率谱密度(PSD)。生成的PSD可以很容易地映射到原始数据的PSD中。为此,Burg的技术已应用于自相关,并给出了应用结果。
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