Paper Info
Title
Penalized Partially Linear Models Using Orthonormal Wavelet Bases With An Application To Fmri Time Series
Abstract
In this paper, we consider modeling the non-parametric component in partially linear models (PLM) using orthogonal wavelet expansions. We introduce a regularized estimator of the non-parametric part in the wavelet domain. The key innovation here is that the non-parametric part can be efficiently estimated by choosing an appropriate penalty function for which the hard and soft thresholding estimators are particular cases. This avoids excessive bias in estimating the parametric component. We give an efficient estimation algorithm. A large scale simulation study is also conducted to illustrate the finite sample properties of the estimator. The estimator is finally applied to real neurophysiological C, functional MRI data sets that are suspected to contain both smooth and transient drift features.
Year
DOI
Venue
2004
10.1109/ISBI.2004.1398752
2004 2ND IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: MACRO TO NANO, VOLS 1 and 2
Keywords
Field
DocType
time series,brain mapping,independent component analysis,vectors,estimation theory,wavelet transforms,neurophysiology,polynomials,linear regression,magnetic resonance imaging
Orthogonal wavelet,Pattern recognition,Linear model,Computer science,Nonparametric statistics,Orthonormal basis,Artificial intelligence,Estimation theory,Wavelet,Wavelet transform,Estimator
Conference
Citations 
PageRank 
References 
0
0.34
2
Authors
2
Name
Order
Citations
PageRank
Mohamed-Jalal Fadili123818.80
Ed Bullmore21331150.94