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Non‐parametric regression with wavelet kernels
Applied Stochastic Models in Business and Industry, 2005AbstractThis paper introduces a method to construct a reproducing wavelet kernel Hilbert spaces for non‐parametric regression estimation when the sampling points are not equally spaced. Another objective is to make high‐dimensional wavelet estimation problems tractable.
Rakotomamonjy, Alain +2 more
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Non-Parametric Regression Methods
Computational Management Science, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Smoothness in Bayesian Non-parametric Regression with Wavelets
Methodology And Computing In Applied Probability, 1999zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Di Zio, Marco, Frigessi, Arnoldo
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Modeling by Non-Parametric Regression
1997In the previous chapter on the adaptive modeling of natural laws it was stated that tasks associated with such modeling included the estimation and storage of the probability distribution, as well as the development of a method for its effective application.
Igor Grabec, Wolfgang Sachse
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Efficient error variance estimation in non‐parametric regression
Australian & New Zealand Journal of Statistics, 2020SummaryError variance estimation plays a key role in the analysis of homogeneous non‐parametric regression models. For a random design model, most methods in the literature for error variance estimation assume the independence between the predictor variable X and the error ε.
Zhijian Li, Wei Lin
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Non-Parametric Regression in Curve Fitting
The Statistician, 1992In the present paper we consider a number of non-parametric regression methods for smoothing curves. These comprise (i) series estimators (classical Fourier and polynomial), (ii) cubic smoothing splines and (iii) least-squares splines. The methods discussed in this paper are intended to promote the understanding and extend the practicability of the non-
Mohamed A. A. Moussa, Mohamed Y. Cheema
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Efficient Bandwidth Selection in Non‐parametric Regression
Scandinavian Journal of Statistics, 2003In this paper we use non‐parametric local polynomial methods to estimate the regression function, m(x). Y may be a binary or continuous response variable, and X is continuous with non‐uniform density. The main contributions of this paper are the weak convergence of a bandwidth process for kernels of order (0,k), k=2j, j≥1 and the proposal of a local ...
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Non parametric Regression Analysis
2001Abstract The Department of Obstetrics, Gynecology, and Reproductive Health at the University of California, San Francisco (UCSF) maintains a comprehensive database containing data on mothers and their newborn infants. To begin to understand the problems associated with mothers who fail to gain normal amounts of weight during pregnancy, a
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Simple Transformation Techniques for Improved Non‐parametric Regression
Scandinavian Journal of Statistics, 1997We propose and investigate two new methods for achieving less bias in non‐ parametric regression. We show that the new methods have bias of order h4, where h is a smoothing parameter, in contrast to the basic kernel estimator’s order h2. The methods are conceptually very simple.
Park, B. U. +5 more
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Optimization in Non-Parametric Regression
1984Non parametric regression is approached through linear estimation, a less restrictive view than the kernel approach since the solution can be adaptative for any pattern of distribution of the abscissae. Local polynomial regression happens to be optimal in the sense of minimum variance for a given order of biais reduction.
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