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Estimating the Fisher’s Scoring Matrix Formula from Logistic Model

Received: 21 October 2013     Published: 20 November 2013
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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.

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

Keywords

GDM, Logistic Regression, Dichotomous, Fisher Scoring, Newton-Raphson, Risk factors

References
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[4] Cai, T. and C. S. Moskowitz. (2004). "Semi-parametric estimation of the binormal ROC curve for a continuous diagnostic test,"Biostatistics, vol. 5, no. 4, pp. 573–586.
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[7] Farbod, D., Ebrahimpour, M., Ghayournoradi, Z., (2010), Maximum Likelihood Estimation for Distribution Generated by Cauchy Stable Law, International Journal of Mathematics and Computation, Vol. 7, No. J10, pp. 23-28.
[8] Fox John(2005).Maximum-likelihood Estimation of the Logistic Regression Model.UCLA/CCPR.Notes.
[9] Friedman S, Khoury-Collado F, Dalloul M, Sherer DM, Abulafia O. Glucose challenge test threshold values in screening for gestational diabetes among black women. Am J Obstet Gynecol 2006; 194: e46-8.
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[13] Khurram, M. Paracha, S.J. Khar,H.T. Hasan,Z. (2006) Obesity related complications in 100 obese subjects and their age matched controls. J Pak Med Assoc, 56(2): 50-3
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[16] Mengesha, B. and Qadeer, A. (1997) Heritability of DM. JPMA; 74(1): 37-40
[17] Morris, R.D. Rimm, D.L. Hartz,A.J. Kalkhoff,R.K. Rimm,A.A (1989) Obesity and F.H of NIDDM, A cross-sectional study. Amj Epidemiol; 130(1): 112-21
[18] Miyakoshi K, Tanaka M, Ueno K, Uehara K, Ishimoto H, Yoshimura Y. Cutoff value of 1 h, 50 g glucose challenge test for screening of gestational diabetes mellitus in a Japanese population. Diabetes Res Clin Pract 2003; 60: 63-7.
[19] Pepe, M. S.(2003). The Statistical Evaluation of Medical Tests for Classification and Prediction, Oxford University Press, New York, NY, USA.
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[21] Shafi, S. Rao, M.H. Bukhsh,I. Soomro,M. (2004) The effect of life style and socio-economic factors in the development of obesity in young adults. Pakistan J. Med. Res. 43(2):
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Cite This Article
  • 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

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    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

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    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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  • @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}
    }
    

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  • 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
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    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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Author Information
  • Department of Industrial Mathematics and Applied Statistics, Ebonyi State University Abakaliki, Nigeria

  • Department of Applied Statistics, Nnamdi Azikiwe University, Awka Nigeria

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