Proceedings of the 2019 International Conference on Modeling, Simulation and Big Data Analysis (MSBDA 2019)

Statistical Diagnostics of Reproductive Dispersion Model Based on Pena Distance

Authors
Lin Dai, Hanchi Lu, Liucang Wu
Corresponding Author
Liucang Wu
Available Online August 2019.
DOI
10.2991/msbda-19.2019.10How to use a DOI?
Keywords
Pena distance, Reproductive dispersion model, Data deletion model, Statistical diagnostics
Abstract

The statistical diagnostics of the reproductive dispersion model based on the Pena distance is discussed. The expression of the Pena distance under the reproductive dispersion model is obtained, and its properties are discussed, and the discrimination of high-leverage outlier is obtained. In addition, comparing the Pena distance with the Cook distance, the conclusion that the Pena distance is better than the Cook distance under certain conditions is obtained. The model and method are scientific and reasonable through the case example analysis.

Copyright
© 2019, 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 2019 International Conference on Modeling, Simulation and Big Data Analysis (MSBDA 2019)
Series
Advances in Computer Science Research
Publication Date
August 2019
ISBN
978-94-6252-784-3
ISSN
2352-538X
DOI
10.2991/msbda-19.2019.10How to use a DOI?
Copyright
© 2019, 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  - Lin Dai
AU  - Hanchi Lu
AU  - Liucang Wu
PY  - 2019/08
DA  - 2019/08
TI  - Statistical Diagnostics of Reproductive Dispersion Model Based on Pena Distance
BT  - Proceedings of the 2019 International Conference on Modeling, Simulation and Big Data Analysis (MSBDA 2019)
PB  - Atlantis Press
SP  - 60
EP  - 64
SN  - 2352-538X
UR  - https://doi.org/10.2991/msbda-19.2019.10
DO  - 10.2991/msbda-19.2019.10
ID  - Dai2019/08
ER  -