Results 261 to 270 of about 1,663,953 (297)

Smoothing parameter selection for smooth distribution functions

Journal of Statistical Planning and Inference, 1993
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The smoothing parameter, confidence interval and robustness for smoothing splines

Journal of Nonparametric Statistics, 2005
Diagnostic measure for nonparametric regression using splines is given. The measure which incorporates important information provided by the smoothing parameter has the potential of identifying ‘unusual’ observations. These influential observations can substantially influence the global behavior of the fitted curve.
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Enhancement of spatially adaptive smoothing splines via parameterization of smoothing parameters

Computational Statistics & Data Analysis, 2011
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Dongik Jang, Hee-Seok Oh
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How to Select the Smoothing Parameter?

1989
From the results given in the previous sections it appeared that the bandwidth h played a dominant role in the behaviour of kernel estimates for regression, density or hazard function estimation.
Lázió Györfi, Wolfgang Härdle, Pascal Sarda, Philippe Vieu   +3 more
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Algorithms for Optimal Smoothing Parameter

2001
As before, let the linear continuous operators A : X → Z, T : X → Y be defined in the Hilbert spaces, z be an element of the space Z. Present as in Chapter 1 the variational principle for the interpolating spline σ ∈ X in the following way $$\sigma = \arg \mathop {\min }\limits_{u \in X,{A_u} = z} ||{T_u}||Y$$ (12.1) and for the smoothing ...
Anatoly Yu. Bezhaev, Vladimir A. Vasilenko   +1 more
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Smooth dependence on parameters of solutions of variational inequalities

Nonlinear Analysis: Theory, Methods & Applications, 2005
The authors present a result for variational inequalities on closed convex sets in Hilbert spaces that ensures the smooth dependence of the solution with respect to a parameter running in a normed space. The approach consists in showing that, under the imposed assumptions, the variational inequality is locally equivalent to a smooth equation and then ...
Eisner, J. (Jan), Kučera, M. (Milan), Recke, L.   +2 more
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Estimation, Prediction, and Smoothing in Discrete Parameter Systems

IEEE Transactions on Computers, 1970
Deterministic and probabilistic sequential machine theory is used to solve the estimation, prediction, and smoothing problem encountered in noisy discrete parameter systems such as digital data processors and information processing systems. Using Bayes' theorem, the equations describing the ideal estimator, predictor, and smoother are developed.
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A PARAMETER-SPACE OF A CHUA SYSTEM WITH A SMOOTH NONLINEARITY

International Journal of Bifurcation and Chaos, 2009
In this paper we investigate, via numerical simulations, the parameter space of the set of autonomous differential equations of a Chua oscillator, where the piecewise-linear function usually taken to describe the nonlinearity of the Chua diode was replaced by a cubic polynomial.
Holokx A. Albuquerque, Paulo C. Rech
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Iterative estimates for a smoothing parameter

Statistics & Probability Letters, 1995
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Inference strategies for the smoothness parameter in the Potts model

2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 2013
2539
Javier Gimenez 0002, Alejandro César Frery, Ana Georgina Flesia   +2 more
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