A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constrained Restoration and the Recovery of Discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Series estimation of semilinear models
Journal of Multivariate Analysis
Local linear estimation in partly linear models
Journal of Multivariate Analysis
Analysis of Half-Quadratic Minimization Methods for Signal and Image Recovery
SIAM Journal on Scientific Computing
IEEE Transactions on Signal Processing
Deterministic edge-preserving regularization in computed imaging
IEEE Transactions on Image Processing
Nonlinear image recovery with half-quadratic regularization
IEEE Transactions on Image Processing
Local influence for Student-t partially linear models
Computational Statistics & Data Analysis
Robust evaluation for quality of experience in crowdsourcing
Proceedings of the 21st ACM international conference on Multimedia
| Hi-index | 0.00 |
This paper is concerned with a semiparametric partially linear regression model with unknown regression coefficients, an unknown nonparametric function for the non-linear component, and unobservable Gaussian distributed random errors. We present a wavelet thresholding based estimation procedure to estimate the components of the partial linear model by establishing a connection between an l 1-penalty based wavelet estimator of the nonparametric component and Huber's M-estimation of a standard linear model with outliers. Some general results on the large sample properties of the estimates of both the parametric and the nonparametric part of the model are established. Simulations are used to illustrate the general results and to compare the proposed methodology with other methods available in the recent literature.