The kernel density estimator
Some Improvement on Convergence Rates of Kernel Density Estimator
... reduced kernel estimator (GEBRK) f n given in ...ordinary kernel density estimator (OK) f n in ...ordinary kernel esti- mator (GEOK) f n in ...the kernel estimators ...
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Asymmetric Kernel Density Estimator for Length Biased Data.
... the kernel density estimator for f (x) obtained by using the corresponding estimator for g(x) and the relation ...mean estimator as proposed by Cox[7]. Cox[7] also gave a direct ...
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A Simple Deconvolving Kernel Density Estimator when Noise is Gaussian
... by kernel deconvolution when noise is ...ordinary kernel density ...new estimator compared to the exact one for several sample sizes and target ...
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Some Asymptotic Results of Kernel Density Estimator in Length-Biased Sampling
... 2 Department of Statistics, Faculty of Sciences, University of Birjand, Birjand, Islamic Republic of Iran Received: 10 September 2012 / Revised: 6 February 2013 / Accepted: 6 May 2013 Abstract In this paper, we prove the ...
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Asymptotic Behaviors of Nearest Neighbor Kernel Density Estimator in Left-truncated Data
... Abstract Kernel density estimators are the basic tools for density estimation in non- parametric ...neighbor kernel estimators represent a special form of kernel density ...
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A Berry-Esseen Type Bound for the Kernel Density Estimator of Length-Biased Data
... Received: 12 July 2014 / Revised: 6 June 2015 / Accepted: 10 August 2015 Abstract Length-biased data are widely seen in applications. They are mostly applicable in epidemiological studies or survival analysis in medical ...
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Analytical Plug-In Method for Kernel Density Estimator Applied to Genetic Neutrality Study
... Correspondence should be addressed to Samir Saoudi, [email protected] Received 21 November 2007; Accepted 1 May 2008 Recommended by Jar-Ferr Yang The plug-in method enables optimization of the bandwidth of ...
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A Berry Esseen type bound for the kernel density estimator based on a weakly dependent and randomly left truncated data
... Abstract In many applications, the available data come from a sampling scheme that causes loss of information in terms of left truncation. In some cases, in addition to left truncation, the data are weakly dependent. In ...
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The Berry Esséen bounds for kernel density estimator under dependent sample
... nonparametric estimator of a distribution based on a sample of obser- vations is the empirical distribution, and the most popular method of nonparametric den- sity estimation is the kernel ...for ...
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Supporting Information. X-Entropy: A Parallelized Kernel Density. Estimator with Automated Bandwidth. Selection to Calculate Entropy
... 1 Other Approaches for Entropy Calculation We use a parameter free method to calculate the entropy. We first perform a KDE, to estimate the underlying PDF from the data. However, using a KDE and integrating the ...
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Large and Moderate Deviations Principles for Recursive Kernel Estimator of a Multivariate Density and its Partial Derivatives
... Our first aim is to establish pointwise LDP for the recursive kernel density estimator f n . It turns out that expliciting the rate function in this case is more complex than either for the ...
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Kernel Density Metric Learning
... This paper contains the following contributions. We in- troduce KDML, a nonlinear metric learning algorithm, by proposing a novel nonlinear mapping which provides a good similarity measure based on kernel ...
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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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Kernel deconvolution density estimation
... different kernel choices in decon- volution estimation with panel data across the four distributional cases of ordinary smooth/ supersmooth (OS/SS) signal and OS/SS noise (with m = 2 replicates per individual and ...
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A convolution estimator for the density of nonlinear regression observations
... unknown density function has been widely ...convolution estimator for the density of the responses in a nonlinear regression ...convolution estimator is of order n − 1 . This is faster than ...
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Robust Kernel Density Function Estimation
... of density function as initial similarity (or distance) measure of observations with the ...estimate density function ...the kernel function to formulate the robust density function ...
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Kernel Density Estimation for Dynamical Systems
... the kernel density estimation problem from a technical as well as practical view and also brings inherent ...the kernel density estimator (1) is proceeded by decomposing the error term ...
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Shape constrained kernel density estimation
... likelihood estimator is first discussed by Grenander (1956) and Groeneboom ...monotone estimator based on least squares estimation where the data is first smoothed by an unconstrained kernel ...
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PLUG-IN BANDWIDTH SELECTOR FOR THE KERNEL RELATIVE DENSITY ESTIMATOR
... relative density. Relative density estimates can provide more detailed information about the performance of a diagnostic test which can be useful not only in comparing different tests but also in designing ...
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Open-cluster density profiles derived using a kernel estimator
... radial density profiles in open clusters are derived using a kernel estimator ...spatial density profile. The evaluation of spatial density profiles is tested against open-cluster ...
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