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Vitamin D Deficiency and Obsessive-Compulsive Disorder Severity: A Cross-Sectional Study. [PDF]

Life (Basel)
Marazziti D, Mucci F, Gambini M, Fazio E, Cazzato L, Carbone MG, Gurrieri R.   +6 more
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Non‐parametric Regression for Circular Responses

Scandinavian Journal of Statistics, 2012
Abstract.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, PANZERA, AGNESE, CHARLES C. TAYLOR   +2 more
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Testing for additivity in non‐parametric regression

Canadian Journal of Statistics, 2016
AbstractThis 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, Staicu, Ana-Maria, Maity, Arnab   +2 more
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Non‐parametric regression with wavelet kernels

Applied Stochastic Models in Business and Industry, 2005
AbstractThis 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, Mary, Xavier, Canu, Stéphane   +2 more
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Non-Parametric Regression Methods

Computational Management Science, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Non‐parametric Regression with Dependent Censored Data

Scandinavian Journal of Statistics, 2008
Abstract.  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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Modeling by Non-Parametric Regression

1997
In 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, 1999
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Di Zio, Marco, Frigessi, Arnoldo
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Efficient error variance estimation in non‐parametric regression

Australian & New Zealand Journal of Statistics, 2020
SummaryError 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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Efficient Bandwidth Selection in Non‐parametric Regression

Scandinavian Journal of Statistics, 2003
In 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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