Year 2019,
Volume: 48 Issue: 2, 580 - 591, 01.04.2019
W. A. Hassanein
,
N. M. Kilany
References
- Agresti, A., “Foundations of Linear and Generalized Linear Models”. John Wiley & Sons,
Inc., Hoboken, New Jersey, 2015.
- Atkinson, A., “DT-Optimum Designs for Model Discrimination and Parameter Estimation”.
Journal of Statistical Planning and Inference, 138, 56-64, 2008.
- Atkinson, A., Donev, A. and Tobias, R., “Optimum Experimental Designs, with SAS”.
Oxford university press, New York, 2007.
- Boyd, S. and Vandenberghe, L., “Convex Optimization”. Cambridge University Press, Cambridge,
2004.
- Chang, F. and Heiligers, B., “E-Optimal Designs for Polynomial Regression without Intercept”.
Journal of Statistical Planning and Inference, 55, 371-387, 1996.
- Clyde, M. and Chaloner, K., “The Equivalence of Constrained and Weighted Designs in
Multiple Objective Design Problems”. Journal of the American Statistical Association, 91,
1236-1244, 1996.
- Corana, A., Marchesi, M., Martini, C. and Ridella, S., “Minimizing Multimodal Functions
of Continuous Variables with the ‘Simulated Annealing’ Algorithm”. ACM Trans. Math.
Software, 13, 262–280, 1987.
- Denman, N. G., McGree, J. M., Eccleston, J. A. and Duffull, S. B., “Design of Experiments
for Bivariate Binary Responses Modelled by Copula Functions”. Computational Statistics
& Data Analysis, 55, 1509-1520, 2011.
- Dette, H., “A Note on E-Optimal Designs for Weighted Polynomial Regression”. Annals of
Statistics, 21, 767-771, 1993.
- Dette, H. and Studden, W., “Geometry of E-Optimality”. Annals of Statistics, 21, 416-433,
1993.
- Dette, H. and Haines, L., “E-optimal Designs for Linear and Nonlinear Models with two
Parameters”. Biometrika, 81, 739–754, 1994.
- Dette, H., Melas,V. and Pepelyshev, A., “Local c-optimal and E-optimal Designs for Exponential
Regression Models”. AISM, 58: 407–426, 2006.
- Dette, H. and Wong, W.K., “E-optimal designs for the Michaelis Menten Model”. Statistics
&Probability Letters, 44, 405–408, 1999.
- Ehrenfeld, E., “On the Efficiency of Experimental Design”. Annals of Mathematical Statistics,
26, 247–255, 1955.
- Heiligers, B., "Computing E–Optimal Polynomial Regression Designs”. Journal of Statistical
Planning and Inference, 55, 219–233, 1996.
- Heiligers, B., "E–Optimal Designs for Polynomial Spline Regression”. Journal of Statistical
Planning and Inference, 75, 159-172, 1998.
- Kilany, N. M., “Optimal Designs for Probability-based Optimality, Parameter Estimation
and Model Discrimination”. Journal of the Egyptian Mathematical Society, 25, 212- 215,
2017.
- Kilany, N. M. and Hassanein, W. A., “AP- Optimum Designs for Minimizing the Average
Variance and Probability-Based Optimality”. REVSTAT – Statistical Journal, In Press, 2017.
- Mcgree, J. M. and Eccleston, J. A., “Probability-Based Optimal Design”. Australian and
New Zealand Journal of Statistics, 50 (1), 13- 28, 2008.
- McGree, J. M., Duffull, S. B. and Eccleston, J. A., “Compound Optimal Design Criteria for
Nonlinear Models”. Journal of Biopharmaceutical Statistics, 18, 646-661, 2008.
- Mwan, D. M., Kosgei, M. K. and Rambaei, S. K., “DT- optimality Criteria for Second Order
Rotatable Designs Constructed Using Balanced Incomplete Block Design”. British Journal
of Mathematics & Computer Science, 22(6), 1-7, 2017.
- Pukelsheim, F. and Studden, W., “E-Optimal Designs for Polynomial Regression”. Annals
of Statistics, 21, 402-415, 1993.
- Wald, A., “On the Efficient Design of Statistical Investigation”. Annals of Mathematical
Statistics, 14, 134–140, 1943.
DE- and EDP$_{M}$- compound optimality for the information and probability-based criteria
Year 2019,
Volume: 48 Issue: 2, 580 - 591, 01.04.2019
W. A. Hassanein
,
N. M. Kilany
Abstract
Several optimality criteria have been considered in the literature as information-based criteria. The probability- based criteria have been recently proposed for maximizing the probability of a desired outcome. However, designs that are optimal for the information- based criteria may be inadequate for probability- based criteria. This paper introduces the DE- and EDP${}_{M}$ -- optimum designs for multi aims of optimality for Generalized Linear Models (GLMs). An equivalence theorem is proved for both compound criteria. Finally, two numerical examples are given to illustrate the potentiality of the proposed compound criteria.
References
- Agresti, A., “Foundations of Linear and Generalized Linear Models”. John Wiley & Sons,
Inc., Hoboken, New Jersey, 2015.
- Atkinson, A., “DT-Optimum Designs for Model Discrimination and Parameter Estimation”.
Journal of Statistical Planning and Inference, 138, 56-64, 2008.
- Atkinson, A., Donev, A. and Tobias, R., “Optimum Experimental Designs, with SAS”.
Oxford university press, New York, 2007.
- Boyd, S. and Vandenberghe, L., “Convex Optimization”. Cambridge University Press, Cambridge,
2004.
- Chang, F. and Heiligers, B., “E-Optimal Designs for Polynomial Regression without Intercept”.
Journal of Statistical Planning and Inference, 55, 371-387, 1996.
- Clyde, M. and Chaloner, K., “The Equivalence of Constrained and Weighted Designs in
Multiple Objective Design Problems”. Journal of the American Statistical Association, 91,
1236-1244, 1996.
- Corana, A., Marchesi, M., Martini, C. and Ridella, S., “Minimizing Multimodal Functions
of Continuous Variables with the ‘Simulated Annealing’ Algorithm”. ACM Trans. Math.
Software, 13, 262–280, 1987.
- Denman, N. G., McGree, J. M., Eccleston, J. A. and Duffull, S. B., “Design of Experiments
for Bivariate Binary Responses Modelled by Copula Functions”. Computational Statistics
& Data Analysis, 55, 1509-1520, 2011.
- Dette, H., “A Note on E-Optimal Designs for Weighted Polynomial Regression”. Annals of
Statistics, 21, 767-771, 1993.
- Dette, H. and Studden, W., “Geometry of E-Optimality”. Annals of Statistics, 21, 416-433,
1993.
- Dette, H. and Haines, L., “E-optimal Designs for Linear and Nonlinear Models with two
Parameters”. Biometrika, 81, 739–754, 1994.
- Dette, H., Melas,V. and Pepelyshev, A., “Local c-optimal and E-optimal Designs for Exponential
Regression Models”. AISM, 58: 407–426, 2006.
- Dette, H. and Wong, W.K., “E-optimal designs for the Michaelis Menten Model”. Statistics
&Probability Letters, 44, 405–408, 1999.
- Ehrenfeld, E., “On the Efficiency of Experimental Design”. Annals of Mathematical Statistics,
26, 247–255, 1955.
- Heiligers, B., "Computing E–Optimal Polynomial Regression Designs”. Journal of Statistical
Planning and Inference, 55, 219–233, 1996.
- Heiligers, B., "E–Optimal Designs for Polynomial Spline Regression”. Journal of Statistical
Planning and Inference, 75, 159-172, 1998.
- Kilany, N. M., “Optimal Designs for Probability-based Optimality, Parameter Estimation
and Model Discrimination”. Journal of the Egyptian Mathematical Society, 25, 212- 215,
2017.
- Kilany, N. M. and Hassanein, W. A., “AP- Optimum Designs for Minimizing the Average
Variance and Probability-Based Optimality”. REVSTAT – Statistical Journal, In Press, 2017.
- Mcgree, J. M. and Eccleston, J. A., “Probability-Based Optimal Design”. Australian and
New Zealand Journal of Statistics, 50 (1), 13- 28, 2008.
- McGree, J. M., Duffull, S. B. and Eccleston, J. A., “Compound Optimal Design Criteria for
Nonlinear Models”. Journal of Biopharmaceutical Statistics, 18, 646-661, 2008.
- Mwan, D. M., Kosgei, M. K. and Rambaei, S. K., “DT- optimality Criteria for Second Order
Rotatable Designs Constructed Using Balanced Incomplete Block Design”. British Journal
of Mathematics & Computer Science, 22(6), 1-7, 2017.
- Pukelsheim, F. and Studden, W., “E-Optimal Designs for Polynomial Regression”. Annals
of Statistics, 21, 402-415, 1993.
- Wald, A., “On the Efficient Design of Statistical Investigation”. Annals of Mathematical
Statistics, 14, 134–140, 1943.