Statistical framework for optimal model choice.
Model selection is a fundamental statistical process that provides systematic methods for choosing the most appropriate model from multiple candidates based on data. This critical component of statistical analysis balances model complexity with predictive accuracy to avoid both underfitting and overfitting.
Faculty studying model selection.
Thomas DiCiccio
Office:
Computing and Information Science Building
Phone:(607) 255-8643
David Ruppert
Andrew Schultz Jr. Professor of Engineering, School of Operations Research and Information Engineering, Professor of Statistics and Data Science
Martin T. Wells
Charles A. Alexander Professor of Statistical Sciences, Director of Undergraduate Studies, Statistics and Data Science
