kernel-based density estimator
Bias reduction in kernel density estimation via Lipschitz condition
... nonparametric kernel based density estimator for which reduction in the order of the bias, relative to the Rosenblatt-Parzen estimator, is attained by imposing global Lipschitz ...
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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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Density Estimation methods based on Mass
... used density estimation methods such as kernel density estimator and k-nearest neighbour density estimator have high time and space complexities which render them difficult to ...
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On improve boundary effect in kernel distribution estimation
... that kernel dis- tribution estimators are not consistent when estimating a distribution near the …nite end points of the ...the kernel density esti- mation context with some ...new kernel type ...
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PERFORMANCE ANALYSIS OF DENSITY ESTIMATION AND MASS ESTIMATION Khushabu Trivedi 1, Parvati Bhurani2
... data. Density estimation is the ubiquitous base modelling mechanism employed for many tasks including clustering, classification, anomaly detection and information ...used density estimation methods such as ...
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On the convergence rates of kernel estimator and hazard estimator for widely dependent samples
... estimate based on WOD ...of estimator of fixed design regression model for WOD ...of kernel density estimator for identically distributed WOD ...recursive kernel estimator ...
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Analytical Plug-In Method for Kernel Density Estimator Applied to Genetic Neutrality Study
... In this paper, we have presented a fast version of the plug- in algorithm which estimates the optimal KDE bandwidth as well as the classical plug-in algorithm. Such a method is based on the optimal kernel ...
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Density Estimation methods based on Mass
... conventional kernel density estimator which shows a bias-variance trade off—the bias decreases as the kernel bandwidth b decreases but this increases the variance; and the reverse is true if ...
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Density Estimation in Infinite Dimensional Exponential Families
... estimator based on minimizing the regularized version of the empirical Fisher divergence, which results in solving a simple finite-dimensional linear ...Our estimator provides a computationally ...
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Asymptotic Confidence Bands for Copulas Based on the Local Linear Kernel Estimator
... for density and distribution function kernel ...to kernel-type estimators of copulas. The existence of kernel-type function estimators should lead to nonparametric estimation by confidence ...
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On Variable Bandwidth Kernel Density And Regression Estimation
... bandwidth kernel density estimator introduced by McKay [16, 17] and the plug-in practical version of the variable bandwidth kernel density estimator with two sequences of ...
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Asymptotic Behaviors of Nearest Neighbor Kernel Density Estimator in Left-truncated Data
... the kernel estimator with bandwidth h n remains correct for the nearest neighbor estimator with the same kernel and k ( n ) = nh n for any > ...criteria. Based on the paper [12], ...
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Some Asymptotic Results of Kernel Density Estimator in Length-Biased Sampling
... the kernel density estimator proposed by Jones [12] for length-biased ...is based on the invariance principle for the empirical processes proved by Horváth ...
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On Transformation Based Circular Density Estimators
... the kernel density function is not the crucial part in a kernel den- sity estimator, any kernel function can guarantee the consistency of the density estima- tion (Wand and ...
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Nonparametric specification tests for stochastic volatility models based on volatility density
... parametric estimator used in this section is the GMM estimator of Meddahi ...GMM estimator is less efficient than the simulated likelihood based method as it only uses moment information, but ...
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Is there a single best estimator? Selection of home range estimators using area-under-the-curve
... Location-based kernel density estimators that are not able to incorporate temporal duration ...second-generation estimator (PKDE) yielded higher AUC than the first-generation estimator ...
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A Berry Esseen type bound for the kernel density estimator based on a weakly dependent and randomly left truncated data
... In this paper we obtained Berry-Esseen type bounds for the kernel density estimator based on left-truncated and strongly mixing data. Here it is concluded that in RLTM, which is also dealing ...
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Some Improvement on Convergence Rates of Kernel Density Estimator
... two kernel density estimators are introduced and ...ordinary kernel density estimator. The second proposed density estimator is a geometric extrapolation of the first bias ...
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Bandwidth selection for kernel density estimation with length-biased data
... the kernel density estimator of Jones (1991) for length-biased data, and we also define two different consistent bootstrap ...rule-of-thumb based on the Normal distribution and two bootstrap ...
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Regularized Data-Based Nonparametric Filtration of Stochastic Signals
... For the strong stationary sequences the nonparametric counterpart of the optimal equation was constructed in the theory of nonparametric signal processing. This approach was developed when the state equation and the ...
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