Title :
A Sieve Bootstrap Method for Correlation Analysis
Author :
Webber, Jonathan R. ; Gupta, Yash P.
Author_Institution :
Dalhousie Univ., Halifax
fDate :
6/1/2007 12:00:00 AM
Abstract :
This note presents a nonparametric sieve bootstrap method for estimating the variance of impulse response coefficients and the process steady-state gain determined via correlation analysis. The bootstrap estimates are demonstrated to be better for small samples than the analytical finite sample variance expression for the simplified form (assuming white noise input) of the Wiener-Hopf equations. Monte Carlo simulations demonstrate that solving the linear equations resulting from the Wiener-Hopf equations can result in a variance reduction.
Keywords :
FIR filters; bootstrapping; correlation methods; estimation theory; nonparametric statistics; predictive control; FIR models; Wiener-Hopf equations; correlation analysis; impulse response coefficient variance estimation; nonparametric sieve bootstrap method; process steady-state gain estimation; Analysis of variance; Covariance matrix; Equations; Finite impulse response filter; Predictive models; Sampling methods; Steady-state; System identification; Uncertainty; White noise; Bootstrap; correlation analysis; impulse response; model uncertainty; system identification;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.2007.899079
