Next Article in Journal
A Consistent Estimator of Nontrivial Stationary Solutions of Dynamic Neural Fields
Previous Article in Journal
Improving the Efficiency of Robust Estimators for the Generalized Linear Model

Predictor Analysis in Group Decision Making

Independent Researcher, 13417 Inverness Rd., Minnetonka, Minneapolis, MN 55305, USA
Received: 13 January 2021 / Revised: 3 February 2021 / Accepted: 4 February 2021 / Published: 9 February 2021
(This article belongs to the Section Regression Models)
Priority vectors in the Analytic Hierarchy Process (AHP) are commonly estimated as constant values calculated by the pairwise comparison ratios elicited from an expert. For multiple experts, or panel data, or other data with varied characteristics of measurements, the priority vectors can be built as functions of the auxiliary predictors. For example, in multi-person decision making, the priorities can be obtained in regression modeling by the demographic and socio-economic properties. Then the priorities can be predicted for individual respondents, profiled by each predictor, forecasted in time, studied by the predictor importance, and estimated by the characteristic of significance, fit and quality well-known in regression modeling. Numerical results show that the suggested approaches reveal useful features of priority behavior, that can noticeably extend the AHP abilities and applications for numerous multiple-criteria decision making problems. The considered methods are useful for segmentation of the respondents and finding optimum managerial solutions specific for each segment. It can help to decision makers to focus on the respondents’ individual features and to increase customer satisfaction, their retention and loyalty to the promoted brands or products. View Full-Text
Keywords: AHP priority as functions; exponential and multinomial models; auxiliary predictors AHP priority as functions; exponential and multinomial models; auxiliary predictors
Show Figures

Figure 1

MDPI and ACS Style

Lipovetsky, S. Predictor Analysis in Group Decision Making. Stats 2021, 4, 108-121.

AMA Style

Lipovetsky S. Predictor Analysis in Group Decision Making. Stats. 2021; 4(1):108-121.

Chicago/Turabian Style

Lipovetsky, Stan. 2021. "Predictor Analysis in Group Decision Making" Stats 4, no. 1: 108-121.

Find Other Styles

Article Access Map by Country/Region

Back to TopTop