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Optimal bandwidth selection for MLS surfaces
2008 IEEE International Conference on Shape Modeling and Applications, 2008We address the problem of bandwidth selection in MLS surfaces. While the problem has received relatively little attention in the literature, we show that appropriate selection plays a critical role in the quality of reconstructed surfaces. We formulate the MLS polynomial fitting step as a kernel regression problem for both noiseless and noisy data ...
Hao Wang +2 more
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Selection of bandwidth for kernel regression
Communications in Statistics - Theory and Methods, 2016The most important factor in kernel regression is a choice of a bandwidth. Considerable attention has been paid to extension the idea of an iterative method known for a kernel density estimate to kernel regression. Data-driven selectors of the bandwidth for kernel regression are considered. The proposed method is based on an optimally balanced relation
Jan Koláček, Ivanka Horová
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Bandwidth selection in robust smoothing
Journal of Nonparametric Statistics, 1993In robust smoothing of regression functions of the type , the theory in automatic bandwidth selection is still lacking. This paper tries to fill the gap by looking at the use of cross-validation methods in robust smoothing. Conjectures regarding the use of a class of robust cross-validation method are made.
Leung, Denis H. Y. +2 more
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On Adaptive Bandwidth Selection for Efficient MIA
2014Recently, a generic DPA attack using the mutual information index as the side channel distinguisher has been introduced. Mutual Information Analysis’s (MIA) main interest is its claimed genericity. However, it requires the estimation of various probability density functions (PDF), which is a task that involves the complicated problem of selecting ...
Carbone, Mathieu +6 more
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Bandwidth selection in smoothing functions
East African Journal of Statistics, 2007A simple criterion for selecting a bandwidth parameter that controls the amount of smoothing in functions is described. The procedure is computationally inexpensive and, hence, worth adopting. We argue that the bandwidth parameter is determined by two factors: the kernel function and the length of the smoothing region.
Kibua, T K, Karuku, M
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OPTIMAL BANDWIDTH SELECTION IN NONLINEAR COINTEGRATING REGRESSION
Econometric Theory, 2020We study optimal bandwidth selection in nonparametric cointegrating regression where the regressor is a stochastic trend process driven by short or long memory innovations. Unlike stationary regression, the optimal bandwidth is found to be a random sequence which depends on the sojourn time of the process. All random sequences $h_{n}$ that lie within
Wang, Qiying, Phillips, Peter C. B.
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Offset Selection for Bandwidth Maximization on Multiple Routes
2018 Annual American Control Conference (ACC), 2018We consider the problem of offset selection for fixed-time signals in a network of arbitrary shape so as to increase the bandwidths that vehicles on multiple routes receive. Assuming that all signals have a common cycle, we utilize the concept of relative path offsets and formulate the problem of maximizing a weighted sum of path bandwidths. This leads
Negar Mehr +4 more
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Path selection and bandwidth allocation in MPLS networks
Performance Evaluation, 2003Multi-protocol label switching extends the IP destination-based routing protocols to provide new and scalable routing capabilities in connectionless networks using relatively simple packet forwarding mechanisms. MPLS networks carry traffic on virtual connections called label switched paths.
James E. Burns +3 more
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Bandwidth Selection in Practice
1991The choice of the bandwidth h is the main problem of kernel density estimation. In some situations it might be quite useful to have a set of estimates corresponding to different bandwidths. Those estimates can highlight different aspects in the structure of the data. However, the presentation and iterpretation of such curves is quite subjective.
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Bandwidth selection for kernel log-density estimation
Computational Statistics & Data Analysis, 2016zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Martin L. Hazelton, Murray P. Cox
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