Absolute value optimization to estimate phase properties of stochastic time series (Corresp.)
Absolute value optimization to estimate phase properties of stochastic time series (Corresp.)
复制标题
估计随机时间序列相位特性的绝对值优化（对应）
DOI：
10.1109/tit.1977.1055668
复制
发表时间：
1977
影响因子：
2.5
通讯作者：
J. Scargle
中科院分区：
文献类型：
--
作者：
J. Scargle
大多数现有的反褶积技术无法确定相位属性的子波从时间序列数据，以确保一个唯一的解决方案，{\em最小相位}通常是假设。它表明，对于一阶移动平均过程，反卷积滤波使用的绝对值范数提供了一个估计的小波形状，具有正确的相位特性时，随机驱动过程是非正常的。数值试验表明，这一结果可能适用于更一般的过程。
Most existing deconvolution techniques are incapable of determining phase properties of wavelets from time series data; to assure a unique solution, {\em minimum phase} is usually assumed. It is demonstrated, for moving average processes of order one, that deconvolution filtering using the absolute value norm provides an estimate of the wavelet shape that has the correct phase character when the random driving process is nonnormal. Numerical tests show that this result probably applies to more general processes.