Fast Selection of Bandwidths for Nonparametric Estimation of the Probability Density of a Two-Dimensional Random Variable with Dependent Components
Author:
Publisher
Allerton Press
Subject
Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation
Link
https://link.springer.com/content/pdf/10.3103/S875669902302005X.pdf
Reference18 articles.
1. A. V. Lapko, V. A. Lapko, and A. V. Bakhtina, ‘‘Application of a nonparametric pattern recognition algorithm to the problem of testing the hypothesis of the independence of variables of multi-valued functions,’’ Meas. Tech. 65, 17–23 (2022). https://doi.org/10.1007/s11018-022-02043-2
2. A. V. Lapko, V. A. Lapko, and A. V. Bakhtina, ‘‘Study of the method for verification of the hypothesis on independence of two-dimensional random quantities using a nonparametric classifier,’’ Optoelectron., Instrum. Data Process. 57, 639–648 (2021). https://doi.org/10.3103/S8756699021060078
3. I. V. Zenko, A. V. Lapko, V. A. Lapko, E. V. Kiryushina, V. N. Vokin, and A. V. Bakhtina, ‘‘A method of sequentially generating a set of components of a multidimensional random variable using a nonparametric pattern recognition algorithm,’’ Komp’yuternaya Opt. 45, 926–933. https://doi.org/10.18287/2412-6179-CO-902
4. M. Rudemo, ‘‘Empirical choice of histogram and kernel density estimators,’’ Scandinav. J. Stat. 9, 65–78 (1982).
5. A. W. Bowman, ‘‘A comparative study of some kernel-based non-parametric density estimators,’’ J. Stat. Comput. Simul. 21, 313–327 (1985). https://doi.org/10.1080/00949658508810822
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