Nonparametric spectral density estimation with missing observations

Abstract

Self-consistency is a fundamental principle in statistics for retaining maximum amount of information in the data. In this paper this principle is applied to develop a new method for nonparametric spectrum estimation with missing data. One major advantage of the proposed method is that it can be coupled with any complete data nonparametric spectrum estimation procedure, including kernel smoothing, wavelet and spline estimators. The practical performance of the method is illustrated by a simulation study.