Proceedings of the 2016 International Conference on Education, Management, Computer and Society

Application of Regression Analysis for Small Samples Based on Bootstrap Method

Authors
Shibo Xin, Bing Ren
Corresponding Author
Shibo Xin
Available Online January 2016.
DOI
10.2991/emcs-16.2016.200How to use a DOI?
Keywords
Multivariate regression analysis; Bootstrap method; Least square method; Regressive coefficients; Small samples
Abstract

Giving bootstrap method of estimation based on multivariate regression analysis when representation of samples are small, the changing multivariate regression equation by bootstrap method of estimation is used as academic multivariate regression equation, and discussing process of bootstrap samples, repeat numbers, principle of bootstrap method of estimation. By bootstrap samples, giving estimate of regressive coefficients, and evaluating validity of bootstrap method of estimation with binomial regression model, in the end, extending the bootstrap method to non-linear regression model.

Copyright
© 2016, 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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Volume Title
Proceedings of the 2016 International Conference on Education, Management, Computer and Society
Series
Advances in Computer Science Research
Publication Date
January 2016
ISBN
978-94-6252-158-2
ISSN
2352-538X
DOI
10.2991/emcs-16.2016.200How to use a DOI?
Copyright
© 2016, 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/).

Cite this article

TY  - CONF
AU  - Shibo Xin
AU  - Bing Ren
PY  - 2016/01
DA  - 2016/01
TI  - Application of Regression Analysis for Small Samples Based on Bootstrap Method
BT  - Proceedings of the 2016 International Conference on Education, Management, Computer and Society
PB  - Atlantis Press
SP  - 816
EP  - 819
SN  - 2352-538X
UR  - https://doi.org/10.2991/emcs-16.2016.200
DO  - 10.2991/emcs-16.2016.200
ID  - Xin2016/01
ER  -