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D-Optimal Design of Experiment for Bivariate Logistic Model Using Various Algorithms
Kupolusi Joseph Ayodele1
- Department of statistics, School of Physical Sciences, Federal University of Technology Akure, Nigeria.
Section:Research Paper, Product Type: Journal-Paper
Vol.9 ,
Issue.6 , pp.50-55, Dec-2022
Online published on Dec 31, 2022
Copyright © Kupolusi Joseph Ayodele . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: Kupolusi Joseph Ayodele, “D-Optimal Design of Experiment for Bivariate Logistic Model Using Various Algorithms,” International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.9, Issue.6, pp.50-55, 2022.
MLA Style Citation: Kupolusi Joseph Ayodele "D-Optimal Design of Experiment for Bivariate Logistic Model Using Various Algorithms." International Journal of Scientific Research in Mathematical and Statistical Sciences 9.6 (2022): 50-55.
APA Style Citation: Kupolusi Joseph Ayodele, (2022). D-Optimal Design of Experiment for Bivariate Logistic Model Using Various Algorithms. International Journal of Scientific Research in Mathematical and Statistical Sciences, 9(6), 50-55.
BibTex Style Citation:
@article{Ayodele_2022,
author = {Kupolusi Joseph Ayodele},
title = {D-Optimal Design of Experiment for Bivariate Logistic Model Using Various Algorithms},
journal = {International Journal of Scientific Research in Mathematical and Statistical Sciences},
issue_date = {12 2022},
volume = {9},
Issue = {6},
month = {12},
year = {2022},
issn = {2347-2693},
pages = {50-55},
url = {https://www.isroset.org/journal/IJSRMSS/full_paper_view.php?paper_id=3015},
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
UR - https://www.isroset.org/journal/IJSRMSS/full_paper_view.php?paper_id=3015
TI - D-Optimal Design of Experiment for Bivariate Logistic Model Using Various Algorithms
T2 - International Journal of Scientific Research in Mathematical and Statistical Sciences
AU - Kupolusi Joseph Ayodele
PY - 2022
DA - 2022/12/31
PB - IJCSE, Indore, INDIA
SP - 50-55
IS - 6
VL - 9
SN - 2347-2693
ER -
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Abstract :
The optimality of a design can be obtained through various methods in practice. A set of values to locate support points to maximize the determinant of information matrix is sought through this method. Three methods are proposed in this study to finding optimality of a design for binary response variable of quadratic logistic regression model. The methods are namely grid search, Sobol sequence and Halton sequence that were considered to determine most efficient method through design efficiency and prediction error variance (PEV). The suggested design methods were evaluated and compared in simulation study of 10000 runs. The grid search approach outperformed the other two methods with the smaller value of PEV. The lower the PEV of a design, the better its performance for prediction.
Key-Words / Index Term :
optimal design, general equivalence theorem, algorithms, quadratic logistic model
References :
[1] Adebola, F.B., Fasoranbaku, O.A. & Kupolusi, J.A. On prediction error variance to determining optimal design of experiment for two variable quadratic logistic models.
Cogent Mathematics & Statistics. https://doi.org/10.1080/25742558.2020.1853888 2020
[2] Bianca Sylvester, Alina Porfire, Marcela Achim, Lucia Rus, Ioan Tomu??. A step
forward towards the development of stable freeze-dried liposomes: a quality by design approach (QbD) Drug Development and Industrial Pharmacy. 2017
[3] Fornius, E. F. D-optimal Designs for Quadratic Logistic Regression Models. International Journal of Statistical Sciences ISSN 1683–5603 Vol. 6 (Special Issue), , 269-303 . 2008
[4] Fornius, E. F. Sequential Designs for Binary Data with the Purpose to Maximize the
Probability of Response. Communications in Statistics - Simulation and Computation. 2008
[5] Hovav A. Dror & David M Steinberg. Robust Experimental Design for Multivariate
Generalized Linear Models , Technometrics. 2006
[6] McCullagh, P. & Nelder, J. A. Generalized Linear Models, 2nd edition. Chapman and Hall, London. P 21-117. 1989
[7] Myers, R. H., D. C. Montgomery, G. G. Vining and T. J. Robinson. Generalized Linear Models with Applications in Engineering and the Sciences. 2nd edition Wiley, New York. 2010
[8] Nelder, J.A. and Wedderburn, R.W.M. Generalized Linear Models. Journal of the Royal Statistical Society. Series A 135, 370-384. 1972
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