A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
Nonlinear approximation of random functions
SIAM Journal on Applied Mathematics
Wavelet regression for random or irregular design
Computational Statistics & Data Analysis
A review of Bayesian neural networks with an application to near infrared spectroscopy
IEEE Transactions on Neural Networks
Functional PLS logit regression model
Computational Statistics & Data Analysis
Editorial: Statistics for Functional Data
Computational Statistics & Data Analysis
Editorial: 2nd Special Issue on Statistical Signal Extraction and Filtering
Computational Statistics & Data Analysis
Gaussian Regularized Sliced Inverse Regression
Statistics and Computing
On sufficient dimension reduction for proportional censorship model with covariates
Computational Statistics & Data Analysis
Estimation of inverse mean: An orthogonal series approach
Computational Statistics & Data Analysis
An adaptive estimation of MAVE
Journal of Multivariate Analysis
Journal of Multivariate Analysis
Estimator selection and combination in scalar-on-function regression
Computational Statistics & Data Analysis
Functional k-means inverse regression
Computational Statistics & Data Analysis
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Two dimensional reduction regression methods to predict a scalar response from a discretized sample path of a continuous time covariate process are presented. The methods take into account the functional nature of the predictor and are both based on appropriate wavelet decompositions. Using such decompositions, prediction methods are devised that are similar to minimum average variance estimation (MAVE) or functional sliced inverse regression (FSIR). Their practical implementation is described, together with their application both to simulated and on real data analyzing three calibration examples of near infrared spectra.