Higher order spectral estimation for random fields

This paper is concerned with the nonparametric estimation of the higher order cumulant spectra of vector-valued stationary random fields onZ ^d by smoothing the periodograms, whereZ is the space of integers and the dimensiond1. We derive the asymptotic cumulant properties of the spectral estimates, and consider an application to multidimensional nonlinear systems identification. Numerical examples with simulated data are provided.     

潜周期模型中频率变点的估计

本文用逐段计算周期图的办法研究了带有频率交点的潜周期模型估计问题，给出了交点数目、位置和潜频率的强相合估计．数值模拟表明本文方法对变点个数和潜频率估计很好，但是要准确估计交点位置需要较大样本量，估计对于噪声水平较高情况仍然有效。     

The SLEX Model of a Non-Stationary Random Process

We propose a new model for non-stationary random processes to represent time series with a time-varying spectral structure. Our SLEX model can be considered as a discrete time-dependent Cramér spectral representation. It is based on the so-called Smooth Localized complex EXponential basis functions which are orthogonal and localized in both time and frequency domains. Our model delivers a finite sample size representation of a SLEX process having a SLEX spectrum which is piecewise constant over time segments. In addition, we embed it into a sequence of models with a limit spectrum, a smoothly in time varying evolutionary spectrum. Hence, we develop the SLEX model parallel to the Dahlhaus (1997, Ann. Statist., 25, 1–37) model of local stationarity, and we show that the two models are asymptotically mean square equivalent. Moreover, to define both the growing complexity of our model sequence and the regularity of the SLEX spectrum we use a wavelet expansion of the spectrum over time. Finally, we develop theory on how to estimate the spectral quantities, and we briefly discuss how to form inference based on resampling (bootstrapping) made possible by the special structure of the SLEX model which allows for simple synthesis of non-stationary processes.     

A Remark on a Fourier Bounding Method of Proof for Convergence of Sums of Periodograms

This paper studies sums of periodograms in a random field setting. In a one dimensional or time series setting these can be studied using a method of cumulants, as done by Brillinger. This method does not carry over well to the random field case. Instead one should apply an argument as used by Rosenblatt. In order to have asymptotically correct confidence intervals, one needs to center these sums properly in the random field case.