Results 231 to 240 of about 196,629 (264)

On the validity of the bootstrap in non-parametric functional regression

Scandinavian Journal of Statistics, 2010
The 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, van Keilegom, Ingrid, Vieu, Philippe   +2 more
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Non-Parametric Regression in Curve Fitting

The Statistician, 1992
In 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
openaire   +1 more source

Optimization in Non-Parametric Regression

1984
Non 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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Non parametric Regression Analysis

2001
Abstract 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, 1997
We 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., Kim, W. C., Ruppert, D., Jones, M. C., Signorini, D. F., Kohn, R.   +5 more
openaire   +1 more source

A note on non-parametric censored regression

Journal of Statistical Computation and Simulation, 1983
A modification of Miller's method of regression for censored data is suggested having better convergence properties.The Stanford Heart transplant data is treated as an ...
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Reference curves based on non‐parametric quantile regression

Statistics in Medicine, 2002
AbstractReference curves which take time into account, such as those for age, are often required in medicine, but simple systematic and efficient statistical methods for constructing them are lacking. Classical methods are based on parametric fitting (polynomial curves). Semi‐parametric methods are also widely used especially in Europe.
Ali, Gannoun, Stéphane, Girard, Christiane, Guinot, Jérôme, Saracco   +3 more
openaire   +2 more sources

Non-Parametric Regression

Journal of the Royal Statistical Society. Series A (General), 1983
S. S. Hussain, P. Sprent
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Optimal Sequential Design in a Controlled Non‐parametric Regression

Scandinavian Journal of Statistics, 2008
Abstract.  In a non‐parametric regression, the heteroscedasticity (dependence of the variance of the regression error on the predictor) can be a serious complication in estimation or visualization of an underlying regression function. If a controlled sampling is permitted, then the statistician can choose the design of predictors which attenuates the ...
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A New Test for the Parametric Form of the Variance Function in Non-Parametric Regression

Journal of the Royal Statistical Society Series B: Statistical Methodology, 2007
Holger Dette, Íngrid Van Keilegom
exaly