A Sieve Bootstrap Method for Correlation Analysis

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 vari...

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Bibliographic Details
Published in:IEEE transactions on automatic control 2007-06, Vol.52 (6), p.1079-1081
Main Authors: Webber, J.R., Gupta, Y.P.
Format: Article
Language:English
Subjects:
Analysis of variance
Applied sciences
Bootstrap
Computer science
control theory
systems
Computer simulation
Control theory. Systems
Correlation analysis
Covariance matrix
Equations
Exact sciences and technology
Finite impulse response filter
Gain
Impulse response
Mathematical analysis
model uncertainty
Modelling and identification
Monte Carlo methods
Predictive models
Sampling methods
Sieves
Steady-state
System identification
Uncertainty
Variance
White noise
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Description
Summary: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.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2007.899079