Journal Article10.32446/0368-1025IT.2019-5-3-6
Bandwidth selection for kernel density estimation in conditions of large samples
A. V. Lapko,V.A. Lapko +1 more
- 01 Jan 2019
- Iss: 5, pp 3-6
6
About: The article was published on 01 Jan 2019. The article focuses on the topics: Kernel density estimation & Bandwidth (signal processing).
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Citations
Estimation of a Nonlinear Functional of Probability Density when Optimizing Nonparametric Decision Functions
A. V. Lapko,V.A. Lapko +1 more
TL;DR: In this paper, a method for estimating the nonlinear functional of the probability density of a two-dimensional random variable is proposed, where the kernel function blur coefficient is presented as a product of an indefinite parameter and standard deviations of random variables.
8
Estimating the Integral of the Square of Derivatives of Symmetric Probability Densities of One-Dimensional Random Variables
TL;DR: In this paper, a method for estimating functionals of derivatives of the probability densities that assumes fulfillment of the following steps is developed on the basis of these results, and the results are confirmed by analyzing data from a numerical simulation.
6
Estimation of the integral of the square of derivatives of symmetric probability densities of one-dimensional random variables
A. V. Lapko,V.A. Lapko +1 more
- 01 Jan 2020
TL;DR: In this paper, a method for estimating functionals from derived probability densities has been developed, which involves the following actions: In the original sample estimated standard deviation of the one-dimensional random variables and the coefficient of antikurtosis, the constants are estimated, which are functionals of the derivatives of the probability density.
5
Dependence Between Histogram Parameters and the Kernel Estimate of a Unimodal Probability Density
TL;DR: In this paper, the authors used the results of an analysis of the asymptotic properties of a nonparametric estimate of the probability density of the Rosenblatt-Parzen type and its modification.
2
Analysis of the Ratio of the Standard Deviations of the Kernel Estimate of the Probability Density with Independent and Dependent Random Variables
TL;DR: In this paper, the influence of information about the dependence of random variables on the approximation properties of a nonparametric estimate of the probability density of the Rosenblatt-Parzen type is determined.