Design of experiments (DOE) is a part of statistics that provides tools for efficient experimentation. Although the subject started in an agricultural context, it is nowadays being applied in many areas, both in science and in industry. A model-oriented view, where some knowledge about the form of the data-generating process is assumed, naturally leads to the so-called optimum experimental design (OED). Due to increasing competition, DOE has become crucial for modern industry, especially for product development. Since different industrial applications of DOE (especially those in pharmaceutical industries) may exhibit very special and varying characteristics, this model oriented approach with its tailor-made solutions is of advantage. Some of the current research lines of the group could be summarize on obtaining Optimal Designs for Nonlinear models. Reliability analysis and censored variables. Multifactorial models. Compartmental models in radiation retention. Pharmacokinetic models.
Clinical Nuclear Medicine 44(10) e548-e558 (2019).Ana M. Garcia Vicente, Julian Perez-Beteta, Mariano Amo-Salas, Francisco J. Pena Pardo, Maikal Villena, Hernan Sandoval, Manuela Mollejo, Rosa Barbella, Chistoph J. Klein, José M. Borras, Ángel M. Soriano, Víctor M. Pérez-García
Conferencia "Aproximaciones alternativas del análisis de supervivencia y fiabilidad"
Defensa de tesis doctoral "Diseño óptimo de experimentos para modelos de ecuaciones simultáneas
Conferencia de Guillermo Sánchez ( Universidad de Salamanca) La apasionante búsqueda de planetas más allá del sistema solar
Nature-Inspired Meta-heuristic Algorithms for Generating Optimal Experimental Designs