Canonical correlation analysis in the definition of weight restrictions for data envelopment analysis

This work investigates the use of canonical correlation analysis (CCA) in the definition of weight restrictions for data envelopment analysis (DEA). With this purpose, CCA limits are introduced into Wong and Beasley's DEA model. An application of the method is made over data from hospitals in 27 Brazilian cities, producing as outputs average payment (average admission values) and percentage of hospital admissions according to disease groups (International Classification of Diseases, 9th Edition), and having as inputs mortality rates and average stay (length of stay after admission (days)). In this application, performance scores were calculated for both the (CCA) restricted and unrestricted DEA models. It can be concluded that the use of CCA-based weight limits for DEA models increases the consistency of the estimated DEA scores (more homogenous weights) and that these limits do not present mathematical infeasibility problems while avoiding the need for subjectively restricting weight variation in DEA.