Covariance approximation of nonlinear regression
- 作者: Labunets L.1,2, Labunets E.3, Lebedeva N.4
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隶属关系:
- Bauman Moscow State Technical University
- Russian New University
- JSB ROSEVROBANK (JSC)
- VTB Bank
- 期: 卷 61, 编号 7 (2016)
- 页面: 789-806
- 栏目: Statistical Radiophysics
- URL: https://journals.rcsi.science/1064-2269/article/view/197107
- DOI: https://doi.org/10.1134/S106422691607007X
- ID: 197107
如何引用文章
详细
A nonlinear regression model on the basis of the covariance approximation of a multidimensional probability distribution is constructed. The model is represented by an expansion in the basis functions in the form of partial derivatives of the logarithm of the joint factor probability distribution. The weight coefficients of the expansion are the covariances of the resulting and explanatory variables. On particular examples, the efficiency of the Bayesian approximation of the proposed regression model in which the factor distribution is described by a finite mixture of ellipsoidally symmetric densities is demonstrated.
作者简介
L. Labunets
Bauman Moscow State Technical University; Russian New University
编辑信件的主要联系方式.
Email: labunets@bmstu.ru
俄罗斯联邦, Vtoraya Baumanskaya ul. 5, Moscow, 105005; ul. Radio 22, Moscow, 105005
E. Labunets
JSB ROSEVROBANK (JSC)
Email: labunets@bmstu.ru
俄罗斯联邦, ul. Vavilova 24, Moscow, 119991
N. Lebedeva
VTB Bank
Email: labunets@bmstu.ru
俄罗斯联邦, ul. Plyushchikha 37, Moscow, 119121