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Non‐parametric Regression for Circular Responses
Scandinavian Journal of Statistics, 2012Abstract.Regression with a circular response is a topic of current interest. We introduce non‐parametric smoothing for this problem. Simple adaptations of a weight function enable a unified formulation for both real‐line and circular predictors, whereas these cases have been tackled by quite distinct parametric methods. Additionally, we discuss various
MARCO DI MARZIO +2 more
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Testing for additivity in non‐parametric regression
Canadian Journal of Statistics, 2016AbstractThis article discusses a novel approach for testing for additivity in non‐parametric regression. We represent the model using a linear mixed model framework and equivalently rewrite the original testing problem as testing for a subset of zero variance components.
Zhang, Yichi +2 more
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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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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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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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Non‐parametric Regression with Dependent Censored Data
Scandinavian Journal of Statistics, 2008Abstract. Let (Xi,Yi) (i=1,…,n) be n replications of a random vector (X,Y ), where Y is supposed to be subject to random right censoring. The data (Xi,Yi) are assumed to come from a stationary α‐mixing process. We consider the problem of estimating the function m(x) =ℰ(φ(Y) | X=x), for some known transformation φ.
El Ghouch, Anouar, van Keilegom, Ingrid
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On the validity of the bootstrap in non-parametric functional regression
Scandinavian Journal of Statistics, 2010The functional nonparametric regression model \(Y=r(\chi)+\varepsilon\) is considered with a functional covariate \(\chi\) and a scalar response \(Y\). A kernel estimate \(\hat r\) is proposed for the regression operator \(r\). A bootstrap methodology is proposed allowing the construction of pointwise confidence intervals for \(r\).
Ferraty, Frédéric +2 more
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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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