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瞬态信号AI 解读

A new method for estimating the parameters describing properties such as the amplitude or velocity of a signal passing across an array of sensors is presented for the case where the signal is a transient plane wave. An appropriate setting for an analysis of the asymptotic properties of the estimates is developed and strong convergence and a central limit theorem are proved. An application to seismological data and some simulations are presented to demonstrate the efficacy of…

瞬态信号AI 解读

A new method for estimating the parameters describing properties such as the amplitude or velocity of a signal passing across an array of sensors is presented for the case where the signal is a transient plane wave. An appropriate setting for an analysis of the asymptotic properties of the estimates is developed and strong convergence and a central limit theorem are proved. An application to seismological data and some simulations are presented to demonstrate the efficacy of…

平稳时间序列的预测方差及相关统计量AI 解读

A spectral estimate of the generalized prediction variance of a stationary time series is considered and a modification which uses a frequency domain regression to allow for the effect of exogenous variables is introduced. Strong consistency and a central limit theorem are proved for this estimate and for several statistics derived from it. The results of some simulations are presented, together with the results of application to two published sets of data

平稳时间序列的预测方差及相关统计量AI 解读

A spectral estimate of the generalized prediction variance of a stationary time series is considered and a modification which uses a frequency domain regression to allow for the effect of exogenous variables is introduced. Strong consistency and a central limit theorem are proved for this estimate and for several statistics derived from it. The results of some simulations are presented, together with the results of application to two published sets of data