Higher-order accurate polyspectral estimation with flat-top lag-windows
Politis, Dimitris Nicolas
SourceAnnals of the Institute of Statistical Mathematics
Google Scholar check
MetadataShow full item record
Improved performance in higher-order spectral density estimation is achieved using a general class of infinite-order kernels. These estimates are asymptotically less biased but with the same order of variance as compared to the classical estimators with second-order kernels. A simple, data-dependent algorithm for selecting the bandwidth is introduced and is shown to be consistent with estimating the optimal bandwidth. The combination of the specialized family of kernels with the new bandwidth selection algorithm yields a considerably improved polyspectral estimator surpassing the performances of existing estimators using second-order kernels. Bispectral simulations with several standard models are used to demonstrate the enhanced performance with the proposed methodology. © 2007 The Institute of Statistical Mathematics, Tokyo.
Showing items related by title, author, creator and subject.
Baldi, S.; Ioannou, Petros A.; Kosmatopoulos, E. B. (2012)A recently proposed adaptive control scheme with mixing involves the use of precalculated candidate controllers whose output is weighted on the basis of the parameter estimates generated by an online parameter estimator. ...
Nonparametric regression estimation based on spatially inhomogeneous data: Minimax global convergence rates and adaptivity Antoniadis, Anestis; Pensky, M.; Sapatinas, Theofanis (2014)We consider the nonparametric regression estimation problem of recovering an unknown response function f on the basis of spatially inhomogeneous data when the design points follow a known density g with a finite number of ...
Charalambous, Charalambos D.; Farhadi, A.; Djouadi, S. M. (2002)This paper presents an action functional, sample path optimization technique, for formulating and solving nonlinear discrete-time stochastic H∞ estimation problems. These H∞ problems are formulated as minimax dynamic games ...