Combined Identification and Prediction Algorithms

DOI

10.2991/itsmssm-17.2017.51How to use a DOI?

Keywords

Nadaraya-Watson nonparametric estimate, parametric estimate, a prior guess, regression, combined algorithm, identification, prediction, bootstrap.

Abstract

In many applied problems it is required to construct a mathematical model of the dependence of output variables on input variables of the stochastic object. To solve this problem, both parametric and nonparametric approaches are used. Each of these approaches has advantages and disadvantages. In the paper, we consider combined algorithms for the identification and prediction of stochastic objects using jointly nonparametric and parametric estimates of regression.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yury Dmitriev
AU  - Gennady Koshkin
AU  - Vadim Lukov
PY  - 2017/12
DA  - 2017/12
TI  - Combined Identification and Prediction Algorithms
BT  - Proceedings of the IV International research conference "Information technologies in Science, Management, Social sphere and Medicine" (ITSMSSM 2017)
PB  - Atlantis Press
SP  - 244
EP  - 247
SN  - 2352-538X
UR  - https://doi.org/10.2991/itsmssm-17.2017.51
DO  - 10.2991/itsmssm-17.2017.51
ID  - Dmitriev2017/12
ER  -