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.
James Booth
Professor of Statistics and Data Science, Department Chair
James Booth
Professor of Statistics and Data Science, Department Chair
Contact
jim.booth@cornell.edu
Jelena Bradic
Professor of Statistics and Data Science
Jelena Bradic
Professor of Statistics and Data Science
Contact
jelena.bradic<at>cornell<dot>edu
Florentina Bunea
Professor of Statistics and Data Science
Thomas DiCiccio
Associate Professor of Social Statistics
Thomas DiCiccio
Associate Professor of Social Statistics
Dan Kowal
Associate Professor of Statistics and Data Science
Dan Kowal
Associate Professor of Statistics and Data Science
Contact
dan.kowal@cornell.edu
David Ruppert
Andrew Schultz Jr. Professor of Engineering, School of Operations Research and Information Engineering, Professor of Statistics and Data Science
David Ruppert
Andrew Schultz Jr. Professor of Engineering, School of Operations Research and Information Engineering, Professor of Statistics and Data Science
Contact
dr24@cornell.edu
Marten Wegkamp
Professor of Statistics and Data Science
Marten Wegkamp
Professor of Statistics and Data Science
Contact
marten.wegkamp@cornell.edu
Martin T. Wells
Charles A. Alexander Professor of Statistical Sciences, Director of Undergraduate Studies, Statistics and Data Science
Martin T. Wells
Charles A. Alexander Professor of Statistical Sciences, Director of Undergraduate Studies, Statistics and Data Science
Contact
mtw1@cornell.edu
