非最小相位线性非高斯序列的替代数据检验

替代数据法作为检验时间序列非线性和混沌的统计方法获得了广泛应用.常用的替代数据法的零假设为“原序列来自(经过单调静态非线性变换的)平稳线性高斯随机过程”.拒绝此假设,并不能说明序列必然来自确定性的非线性动力系统,非最小相位的线性非高斯序列也会导致基于相位随机化的替代数据检验拒绝此假设关键词：替代数据时间序列分析非线性非最小相位

SURROGATE DATA TEST FOR THE LINEAR NON-GAUSSIAN TIME SERIES WITH NON-MINIMUM PHASE

Surrogate data testing is a popular method to detect nonlinearity and chaos in time series and has been vastly used in many applications with erratic time series. The explicit null hypothesis often used is that the time series is generated from a linear, stochastic, Gaussian stationary process, including a possible invertible nonlinear static observation function. It is pointed out that the rejection of such a hypothesis may not only result from an underlying nonlinear or even chaotic system, but also from, e.g., a linear, stochastic, non-Gaussian and non-minimum phase sequence. We investigate the power of the test against non-minimum phase sequence.