Use of kernel density estimators
Comparison of kernel density estimators with assumption on number of modes
... kernel because both exhibit IAE convergence toward 0 and convergence of ˆ z toward z. To study them lengthier, we use the model ( 5 ). Moreover, this selection is also made because of the costs in ...
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Confidence sets in decision problems with kernel density estimators
... a density, have been considered for a long time, see ...can use a result about a so-called inner Kuratowski-Painlev´ e- approximation of solution ...
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Multivariate goodness-of-fit tests based on kernel density estimators
... on kernel density ...on kernel density estimator and some specific normality tests, ...to use analytical approximations for the parameters of proposed test statistics obtained by means ...
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Smoothing Parameters for Recursive Kernel Density Estimators under Censoring
... the use of such bandwidth (h n ) belonging to GS (−a) is that the ratio h n−1 /h n is equal to 1 + a/n + o (1/n), then using such bandwidth and using the assumption (A2) on the bandwidth and on the stepsize, Lemma ...
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Modeling of reactive transport with particle tracking and kernel density estimators
... and kernel density estimators Maryam Rahbaralam Random walk particle tracking methods are a computationally efficient family of meth- ods to solve reactive transport ...The use of a low number ...
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Local Multiplicative Bias Correction for Asymmetric Kernel Density Estimators
... these estimators on three different test densities: a Gamma G ...type estimators which use the gamma as a start ...these estimators is provided by the global parametric ...symmetric ...
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Probabilistic short-term wind power forecasting based on kernel density estimators
... The prediction error approach “adds” uncertainty estima- tion to existing “spot” forecasting systems. Early approaches used global evaluation criteria (such as the standard deviation of forecast errors computed over ...
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Modified home range kernel density estimators that take environmental interactions into account
... Like KDEr and AKDEc, SE-AKDEc, and E-AKDE ex- trapolate the utilization probability. But, contrary to KDEr and AKDEc, they include a dose of mechanism (Eq. 1) into the base kernels of the extrapolation (Eq. 5). This ...
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Averaging of density kernel estimators
... initial estimators, due to Σ being close to ...to use no more than four initial estimators, obtained from different methods, in order to reduce linear dependencies (see the discussion in Section ...
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Asymptotics for General Multivariate Kernel Density Derivative Estimators
... normal density is close to being the density which gives the largest optimal amount of smoothing given a fixed variance for density estimation (Terrell, 1990), then (5) corresponds approximately to ...
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Asymptotics for general multivariate kernel density derivative estimators
... a kernel estimator of a multivariate density derivative, and pro- vide the results for mean integrated square convergence both asymptotically and for finite ...We use these normal scale selectors to ...
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Fast Computation of Kernel Estimators
... any density can be approximated arbitrarily well by a member of this ...each density. In order to compare against the binning methods the kernel density estimates are evaluated at an equally ...
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New bandwidth selection for kernel quantile estimators
... Several data-based methods can be made to find the asymptotically optimal band- width h in kernel quantile estimators for Q AK p given by 1.3. One of these methods use derivatives of the ...
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Kernel density estimation on the torus
... series estimators The aim of this section is to compare our methods with other available ...series estimators are natural competitors because we are working in periodic ...of kernel ef- ficiency is ...
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Kernel deconvolution density estimation
... indirect kernel density-based estimator of the characteristic function of the ...deconvolution estimators for multivariate responses pose more computational challenges, in particular for numerical ...
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Deconvoluting kernel density estimators
... The first term in (2.6) can be much larger than the variance component of the integrated mean squared error of an ordinary kernel density estimator.. This is the price paid for not measu[r] ...
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Adaptive warped kernel estimators
... the estimators is also investigated by computing the MISE for the dierent ...least-squares estimators in these cases, whatever the estimation support is, and whatever the chosen distributions ...
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Kernel Mean Shrinkage Estimators
... the kernel function is non-linear, because the estimation happens in the feature space associated with the kernel function rather than in the input ...
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Maxiset in sup-norm for kernel estimators
... our kernel procedures depend on β (see ...non-adaptive kernel procedures when Hölder functions are ...previous kernel procedures from the maxiset point of ...
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On Estimators of a Spectral Density Function
... to use Beltrao and Bloomfield’s (1987) technique for the au- tomatic smoothness parameter selection, he defines a leave-out-one spectrum version for any ...
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