This paper proposes a matrix approach to estimating parameters of logistic regression with a view to estimating the effects of risk factors of gestational diabetic mellitus (GDM). The proposed method of maximum likelihood estimation (MLE) unlike other methods of estimating parameters of non-linear regression is simpler and convergence of parameters is quicker. The odds ratio obtained from the logistic regression were used to interpret the effects of these risk factors on GDM where obesity and F.H as risk factors, were positively associated with GDM. The proposed method was seen to compare favorably with other known methods.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 2, Issue 6) |
| DOI | 10.11648/j.ajtas.20130206.19 |
| Page(s) | 221-227 |
| 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), 2013. Published by Science Publishing Group |
GDM, Logistic Regression, Dichotomous, Fisher Scoring, Newton-Raphson, Risk factors
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APA Style
Okeh UM, Oyeka I. C. A. (2013). Estimating the Fisher’s Scoring Matrix Formula from Logistic Model. American Journal of Theoretical and Applied Statistics, 2(6), 221-227. https://doi.org/10.11648/j.ajtas.20130206.19
ACS Style
Okeh UM; Oyeka I. C. A. Estimating the Fisher’s Scoring Matrix Formula from Logistic Model. Am. J. Theor. Appl. Stat. 2013, 2(6), 221-227. doi: 10.11648/j.ajtas.20130206.19
AMA Style
Okeh UM, Oyeka I. C. A. Estimating the Fisher’s Scoring Matrix Formula from Logistic Model. Am J Theor Appl Stat. 2013;2(6):221-227. doi: 10.11648/j.ajtas.20130206.19
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Download@article{10.11648/j.ajtas.20130206.19,
author = {Okeh UM and Oyeka I. C. A.},
title = {Estimating the Fisher’s Scoring Matrix Formula from Logistic Model},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {2},
number = {6},
pages = {221-227},
doi = {10.11648/j.ajtas.20130206.19},
url = {https://doi.org/10.11648/j.ajtas.20130206.19},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20130206.19},
abstract = {This paper proposes a matrix approach to estimating parameters of logistic regression with a view to estimating the effects of risk factors of gestational diabetic mellitus (GDM). The proposed method of maximum likelihood estimation (MLE) unlike other methods of estimating parameters of non-linear regression is simpler and convergence of parameters is quicker. The odds ratio obtained from the logistic regression were used to interpret the effects of these risk factors on GDM where obesity and F.H as risk factors, were positively associated with GDM. The proposed method was seen to compare favorably with other known methods.},
year = {2013}
}
TY - JOUR T1 - Estimating the Fisher’s Scoring Matrix Formula from Logistic Model AU - Okeh UM AU - Oyeka I. C. A. Y1 - 2013/11/20 PY - 2013 N1 - https://doi.org/10.11648/j.ajtas.20130206.19 DO - 10.11648/j.ajtas.20130206.19 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 221 EP - 227 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20130206.19 AB - This paper proposes a matrix approach to estimating parameters of logistic regression with a view to estimating the effects of risk factors of gestational diabetic mellitus (GDM). The proposed method of maximum likelihood estimation (MLE) unlike other methods of estimating parameters of non-linear regression is simpler and convergence of parameters is quicker. The odds ratio obtained from the logistic regression were used to interpret the effects of these risk factors on GDM where obesity and F.H as risk factors, were positively associated with GDM. The proposed method was seen to compare favorably with other known methods. VL - 2 IS - 6 ER -
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