title: About the full prediction approximation by a lot of partial predictions in case of incomplete data
metadata.name: dc.contributor.author
metadata.value: Alexeyeva, Nina P.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Al-Juboori, Fatema S. Sh.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2022-12-27T12:16:43Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2022-12-27T12:16:43Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2022-12
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Alexeyeva N.P., Al-Juboori F. S. Sh. About the full prediction approximation by a lot of partial predictions in case of incomplete data. Vestnik of Saint Petersburg University. Mathematics. Mechanics. Astronomy, 2022, vol. 9 (67), issue 4, pp. 575–589. https://doi.org/10.21638/spbu01.2022.401 (In Russian)
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/spbu01.2022.401
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/38713
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: In this article, we are talking about the random subspaces method in forecasting under
  the condition of incomplete data and about estimation of a full forecast based on a set of
  partial predictions. Centered partial predictions are considered without loss of generality.
  According to the statistical model, off-diagonal elements in the correlation matrix of partial
  predictions are considered random with known mathematical expectation and variance.
  In case of this random matrix, analytical expressions are obtained for the mathematical
  expectation of the determinant and minors. Based on these results, a class of more accurate
  estimates of the full prediction is constructed, which differ from the mean partial prediction
  by a multipliers that depend on the statistical parameters of the correlation matrix of
  partial predictions. The results of modeling and practical forecasting based on incomplete
  biogeographic data are presented.
metadata.lang: en_GB
metadata.name: dc.description.sponsorship
metadata.value: This work was supported by the Russian Foundation for Basic Research (project no. 20-01-00096).
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Mathematics. Mechanics. Astronomy;Volume 9 (67); Issue 4
metadata.lang: -
metadata.name: dc.subject
metadata.value: the random subspace method
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: statistical model
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: matrix with random elements
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: partial predictions
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: multiple regression
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: About the full prediction approximation by a lot of partial predictions in case of incomplete data
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 575-589.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/38713/1/575-589.pdf
resources.format: Adobe PDF
resources.size: 531,04 kB