Results 161 to 170 of about 12,193,766 (206)

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 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
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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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A Non-parametric Test for Partial Monotonicity in Multiple Regression

Computational Economics, 2013
Partial positive (negative) monotonicity in a dataset is the property that an increase in an independent variable, ceteris paribus, generates an increase (decrease) in the dependent variable. A test for partial monotonicity in datasets could (1) increase model performance if monotonicity may be assumed, (2) validate the practical relevance of policy ...
van Beek, M., Daniëls, H.A.M.
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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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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
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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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An overview on parametric quantile regression models and their computational implementation with applications to biomedical problems including COVID-19 data

Computer Methods and Programs in Biomedicine, 2022
Josmar Mazucheli, Bruna Alves da Silva, André F B Menezes   +2 more
exaly  

A collection of parametric modal regression models for bounded data

Journal of Biopharmaceutical Statistics, 2021
Josmar Mazucheli, Subrata Chakraborty, André F B Menezes   +2 more
exaly