Trispectrum deconvolution of linear processes with randomly missing observations
M. M. Gabr, A. H. Al-Ibrahim · International Journal of Systems Science · 2002
In a large application area of time series analysis - geophysical exploration - the underlying innovations sequence is of primary interest and must be estimated. This sequence is estimated by deconvolving the non-Gaussian stationary time series. The deconvolution of a non-Gaussian non-minimum phase linear process, when some observations are missing according to a point binomial distribution is considered. This analysis tools are the bispectrum and the trispectrum. A Monte Carlo study is performed to illustrate the proposed methods.