Title: Using criterion-based model averaging in two-input multiple response surface methodology problems
Authors: Domingo Pavolo; Delson Chikobvu
Addresses: Department of Mathematical Statistics and Actuarial Sciences, School of Natural and Agricultural Sciences, University of the Free State, Bloemfontein, South Africa ' Department of Mathematical Statistics and Actuarial Sciences, School of Natural and Agricultural Sciences, University of the Free State, Bloemfontein, South Africa
Abstract: Experimental designs in multiple response surface methodology (MRSM) often result in small sample size datasets with associated modelling and model selection problems. Classical model selection criteria are inefficient when using small sample size datasets while the model selection process has inherent uncertainties. Modelling of small sample size datasets below (10 + k), where k is the maximum number of regressors inclusive of the intercept, suffers from credibility problems. In this empirical paper, criterion-based frequentist model-averaging (CBFMA) is proposed as a solution to the small sample size problems of modelling MRSM datasets. We also compare the goodness of fit and prediction accuracy of using CBFMA models versus ordinary least squares (OLS) candidate models. Findings suggest that CBFMA models have good fitness to data and predictive accuracy. Also, the small sample size model selection criteria bias problem is improved on. However, in the MRSM context, CBFMA does not directly solve both criterion and response surface uncertainties, and averaged model estimators have mean squared errors that are greater than the best OLS candidate models.
Keywords: multiple response surface methodology; MRSM; experimental design; all possible regression models; frequentist criterion-based model averaging; small sample size datasets; process optimisation.
International Journal of Operational Research, 2022 Vol.44 No.1, pp.80 - 101
Received: 18 Mar 2019
Accepted: 12 Jun 2019
Published online: 23 May 2022 *