Statistical inference is based generally on some estimates that are functions of the data. Bootstrapping procedure offers strategies to estimate or approximate the sampling distribution of a statistic. Logistics regression model with binary response is commonly used. This paper focuses on the behavior of bootstrapping pseudo - R2 measures in logistic regression model. Simulation and real data results also presented. We conclude and suggest to use either R2M or R2D, since they have convergence in there values.
| Published in | Biomedical Statistics and Informatics (Volume 2, Issue 3) |
| DOI | 10.11648/j.bsi.20170203.13 |
| Page(s) | 107-110 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Logistic Regression, Pseudo - R2, Bootstrap, Logit, Propit
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APA Style
Zakariya Yahya Algamal, Haithem Taha Mohammad Ali. (2017). Bootstrapping Pseudo - R2 Measures for Binary Response Variable Model. Biomedical Statistics and Informatics, 2(3), 107-110. https://doi.org/10.11648/j.bsi.20170203.13
ACS Style
Zakariya Yahya Algamal; Haithem Taha Mohammad Ali. Bootstrapping Pseudo - R2 Measures for Binary Response Variable Model. Biomed. Stat. Inform. 2017, 2(3), 107-110. doi: 10.11648/j.bsi.20170203.13
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Download@article{10.11648/j.bsi.20170203.13,
author = {Zakariya Yahya Algamal and Haithem Taha Mohammad Ali},
title = {Bootstrapping Pseudo - R2 Measures for Binary Response Variable Model},
journal = {Biomedical Statistics and Informatics},
volume = {2},
number = {3},
pages = {107-110},
doi = {10.11648/j.bsi.20170203.13},
url = {https://doi.org/10.11648/j.bsi.20170203.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20170203.13},
abstract = {Statistical inference is based generally on some estimates that are functions of the data. Bootstrapping procedure offers strategies to estimate or approximate the sampling distribution of a statistic. Logistics regression model with binary response is commonly used. This paper focuses on the behavior of bootstrapping pseudo - R2 measures in logistic regression model. Simulation and real data results also presented. We conclude and suggest to use either R2M or R2D, since they have convergence in there values.},
year = {2017}
}
TY - JOUR T1 - Bootstrapping Pseudo - R2 Measures for Binary Response Variable Model AU - Zakariya Yahya Algamal AU - Haithem Taha Mohammad Ali Y1 - 2017/03/31 PY - 2017 N1 - https://doi.org/10.11648/j.bsi.20170203.13 DO - 10.11648/j.bsi.20170203.13 T2 - Biomedical Statistics and Informatics JF - Biomedical Statistics and Informatics JO - Biomedical Statistics and Informatics SP - 107 EP - 110 PB - Science Publishing Group SN - 2578-8728 UR - https://doi.org/10.11648/j.bsi.20170203.13 AB - Statistical inference is based generally on some estimates that are functions of the data. Bootstrapping procedure offers strategies to estimate or approximate the sampling distribution of a statistic. Logistics regression model with binary response is commonly used. This paper focuses on the behavior of bootstrapping pseudo - R2 measures in logistic regression model. Simulation and real data results also presented. We conclude and suggest to use either R2M or R2D, since they have convergence in there values. VL - 2 IS - 3 ER -
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