Results 251 to 260 of about 3,817,143 (297)

First-passage-based boundary estimation in functional data. [PDF]

PLoS One
Kuzu AT, Celik N.
europepmc   +1 more source

A Delay-Aware Switching Framework for Binary Actuator Systems Under Sensing Delay and Measurement Noise. [PDF]

Sensors (Basel)
Ji K.
europepmc   +1 more source

Human behavior alignment to improve the robustness of deep neural networks. [PDF]

Front Artif Intell
Anand B, Krithivasan S.
europepmc   +1 more source

Cortical Microstructural Variations Correlate With Individual Differences in Gamified Exploration-Exploitation Behaviours. [PDF]

Comput Psychiatr
Tyrer A, Nikolova N, Dubois M, Banellis L, Vejlø M, Hauser TU, Allen M.   +6 more
europepmc   +1 more source

On the Selection of the Smoothing Parameter in Poisson Smoothing of Histogram Estimator: Computational Aspects

, 2008
Chaubey, Yogendra P., Sen, Pranab K., Concordia University. Department of Mathematics & Statistics   +2 more
openaire   +1 more source

Boundary-Transition Characterization in High-Speed Underwater Bubble Imaging Under Broadband and Narrow-Band Illumination. [PDF]

Sensors (Basel)
Lu C, Fan S, Yang Y, He J, Cao Y.
europepmc   +1 more source

PruDensNet: a parameter efficient depthwise separable CNN for MRI-based brain tumor classification. [PDF]

Front Med (Lausanne)
Arman M, Samin A, Islam AKMM, Arnob MMR, Islam MJ, Mamoon IA.   +5 more
europepmc   +1 more source

Smoothing Parameter and Model Selection for General Smooth Models Comment

, 2018
openaire   +1 more source

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Smoothing parameter selection for smoothing splines: a simulation study

Computational Statistics and Data Analysis, 2003
Smoothing splines are a popular method for performing nonparametric regression. Most important in the implementation of this method is the choice of the smoothing parameter. This article provides a simulation study of several smoothing parameter selection methods, including two so-called risk estimation methods.
Thomas Lee
exaly   +3 more sources

Exact risk approaches to smoothing parameter selection

Journal of Nonparametric Statistics, 1997
The past decade has seen the development of a large number of second-generational smoothing parameter selectors as a response to the high degree of variability of cross-validatory methods. However, most of these rules rely on asymptotic approximations which make them subject to adverse performance when the approximations are poor.
Matt Wand
exaly   +2 more sources