Wavelet-generalized least squares: a new BLU estimator of linear regression models with 1/f errors.
Long-memory noise is common to many areas of signal processing and can seriously confound estimation of linear regression model parameters and their standard errors. Classical autoregressive moving average (ARMA) methods can adequately address the problem of linear time invariant, short-memory errors but may be inefficient and/or insufficient to secure type 1 error control in the context of fractal or scale invariant noise with a more slowly decaying autocorrelation function. Here we introduce a novel method, called wavelet-generalized least squares (WLS), which is (to a good approximation) the best linear unbiased (BLU) estimator of regression model parameters in the context of long-memory errors. The method also provides maximum likelihood (ML) estimates of the Hurst exponent (which can be readily translated to the fractal dimension or spectral exponent) characterizing the correlational structure of the errors, and the error variance. The algorithm exploits the whitening or Karhunen-Loéve-type property of the discrete wavelet transform to diagonalize the covariance matrix of the errors generated by an iterative fitting procedure after both data and design matrix have been transformed to the wavelet domain. Properties of this estimator, including its Cramèr-Rao bounds, are derived theoretically and compared to its empirical performance on a range of simulated data. Compared to ordinary least squares and ARMA-based estimators, WLS is shown to be more efficient and to give excellent type 1 error control. The method is also applied to some real (neurophysiological) data acquired by functional magnetic resonance imaging (fMRI) of the human brain. We conclude that wavelet-generalized least squares may be a generally useful estimator of regression models in data complicated by long-memory or fractal noise.
Fadili
M J
MJ
Centre for Speech and Language, University of Cambridge, United Kingdom. Jalal.Fadili@greyc.ismra.fr
Bullmore
E T
ET
D000328:Adult; D016477:Artifacts; D001288:Attention / Q000502:physiology; D017352:Echo-Planar Imaging; D017709:Fractals; D006801:Humans; D016018:Least-Squares Analysis; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008297:Male; D011939:Mental Recall / Q000502:physiology*; D009010:Monte Carlo Method; D016571:Neural Networks, Computer; D009778:Occipital Lobe / Q000502:physiology; D010364:Pattern Recognition, Visual / Q000502:physiology; D011597:Psychomotor Performance / Q000502:physiology; D012044:Regression Analysis; D012153:Retention, Psychology / Q000502:physiology*