Results 211 to 220 of about 101,932 (253)

MonotonicityTest: An R Package for Efficient Nonparametric Monotonicity Testing. [PDF]

Obs Stud
Huynh D, Parast L.
europepmc   +1 more source

Confidence interval construction for multivariable truncated spline logistic model (MTSLM). [PDF]

MethodsX
Suriaslan AS, Budiantara IN, Ratnasari V, Aydin D.   +3 more
europepmc   +1 more source

A benchmarking of genomic selection models for predicting grain-yield related traits using haplotype-based and genome-wide association study-based markers in rice. [PDF]

Plant Genome
Hu X, Xian L, Cao G, Liu S, Wang D, Che Y, Huang W, Lin Y, He L.   +8 more
europepmc   +1 more source

Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study

Journal of Statistical Software, 2001
Theofanis Sapatinas, Jérémie Bigot, Anestis Antoniadis   +2 more
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Quantization for Nonparametric Regression

IEEE Transactions on Information Theory, 2008
The authors discuss quantization or clustering of nonparametric regression estimates. The main tools developed are oracle inequalities for the rate of convergence of constrained least squares estimates. These inequalities yield fast rates for both nonparametric (unconstrained) least squares regression and clustering of partition regression estimates ...
László Györfi, Marten H. Wegkamp
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A nonparametric regression method

Nonlinear Analysis: Theory, Methods & Applications, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Huang, M. L., Brill, P. H.
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Robot learning by nonparametric regression

Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94), 1995
We present an approach to robot learning based on a nonparametric regression technique, locally weighted regression. The model of the task to be performed is represented by infinitely many local linear models, i.e., the (hyper-) tangent planes at every query point. Such a model, however, is only generated when a query performed and is not retained. The
Stefan Schaal, Christopher G. Atkeson
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Nonparametric Regression and Classification Part I—Nonparametric Regression

1994
In classical statistics, regression means linear regression. Recently, more flexible regression tools have been developed, that exploit the dramatic increase in computing power and speed. In this paper we describe some of these developments. The main background reference is Hastie and Tibshirani (1990).
T. J. Hastie, R. J. Tibshirani
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Bootstrap Methods in Nonparametric Regression

1991
Bootstrap techniques naturally arise in the setting of nonparametric regression when we consider questions of smoothing parameter selection or error bar construction. The bootstrap provides a simple-to-implement alternative to procedures based on asymptotic arguments.
Mammen, Enno, Härdle, Wolfgang
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Multi-output Nonparametric Regression

2005
Several non-parametric regression methods with various dependent variables that are possibly related are explored. The techniques which produce the best results in the simulations are those which incorporate the observations of the other response variables in the estimator.
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