2022/08/12 by L. Deldossi, Deldossi, L., E. Pesce +3
Computer Science · Decision Sciences · #Advanced Multi-Objective Optimization Algorithms #Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning (stat.ML) #Methodology (stat.ME) #Optimal Experimental Design Methods #Probabilistic and Robust Engineering Design
paper · pdf · doi:10.48550/arxiv.2208.06218
openalex publication_date 2022/08/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Nowadays, in many different fields, massive data are available and for several reasons, it might be convenient to analyze just a subset of the data. The application of the D-optimality criterion can be helpful to optimally select a subsample of observations. However, it is well known that D-optimal support points lie on the boundary of the design space and if they go hand in hand with extreme response values, they can have a severe influence on the estimated linear model (leverage points with high influence). To overcome this problem, firstly, we propose an unsupervised exchange procedure that enables us to select a nearly D-optimal subset of observations without high leverage values. Then, we provide a supervised version of this exchange procedure, where besides high leverage points also the outliers in the responses (that are not associated to high leverage points) are avoided. This is possible because, unlike other design situations, in subsampling from big datasets the response values may be available. Finally, both the unsupervised and the supervised selection procedures are generalized to I-optimality, with the goal of getting accurate predictions.