Let X = {X(t), - [infinity] t [infinity]} be a continuous-time stationary process with spectral density function [phi]X([lambda]) and {[tau]k} be a stationary point process independent of X. Estimates of [phi]X([lambda]) based on the discrete-time observation {X([tau]k), [tau]k} are...

Random processes with almost periodic covariance function are considered from a spectral outlook. Given suitable conditions, spectral estimation problems are discussed for Gaussian processes of this type that are neither stationary nor locally stationary. Spectral mass is concentrated on lines...

An approximate maximum likelihood procedure is proposed for the estimation of parameters in possibly nonminimum phase (noninvertible) moving average processes driven by independent and identically distributed non-Gaussian noise. Under appropriate conditions, parameter estimates that are...

Let X = {X(t), -[infinity]t[infinity]} be a continuous-time stationary process with spectral density [phi]X([lambda]; [theta]), where [theta] is a vector of unknown parameters. Let {[tau]k} be a stationary point process on the real line which is independent of X. The identifiability and the...

A procedure for deconvolution of nonminimum phase non-Guassian time series based on the estimation of higher order (greater than two) spectra is given. This can be applied to the analysis of seismograms. The procedure allows estimation of the wavelet. Knowledge of cumulant spectra of order...