Estimation of the Spectral Density with Assigned Risk. (26th May 2015)

• Record Type: Journal Article
• Authors: Efromovich, Sam
• Is Part Of: Scandinavian journal of statistics. Volume 43:Number 1(2016:Mar.)
• Page Start: 70
• Page End: 82
• Publication Date: 2015-05-26
• Subjects: adaptation, ARMA, Minimax, MISE, sequential, stopping time, time series; Statistics -- Periodicals; 310
• DOI: 10.1111/sjos.12165
• Languages: English
• ISSNs: 0303-6898

Abstract

Abstract: It is well known that adaptive sequential nonparametric estimation of differentiable functions with assigned mean integrated squared error and minimax expected stopping time is impossible. In other words, no sequential estimator can compete with an oracle estimator that knows how many derivatives an estimated curve has. Differentiable functions are typical in probability density and regression models but not in spectral density models, where considered functions are typically smoother. This paper shows that for a large class of spectral densities, which includes spectral densities of classical autoregressive moving average processes, an adaptive minimax sequential estimation with assigned mean integrated squared error is possible. Furthermore, a two‐stage sequential procedure is proposed, which is minimax and adaptive to smoothness of an underlying spectral density.