Several problems arise when attempting to use traditional predictive modeling techniques on ‘big data.’ For instance, multiple linear regression models cannot be used on datasets with hundreds of variables. However several techniques are becoming common tools for selective inference as the need for analyzing big data increases. Forward selection and penalized regression models (such as LASSO, Ridge Regression, and Elastic Net) are simple modifications of multiple linear regression that can provide some guidance on simplifying a model through variable selection. Dimension reducing techniques, such as Partial Least Squares and Principal Components Analysis, are more complex than regression but have the ability to handle highly correlated independent variables. Each of the aforementioned techniques are valuable in predictive modeling if used properly. This paper provides a mathematical introduction to these developments in selective inference. A sample dataset is used to demonstrate modeling and interpretation. Further, the applications to big data, as well as advantages and disadvantages of each procedure, are discussed.
","abstract_html":"<p>Several problems arise when attempting to use traditional predictive modeling techniques on ‘big data.’ For instance, multiple linear regression models cannot be used on datasets with hundreds of variables. However several techniques are becoming common tools for selective inference as the need for analyzing big data increases. Forward selection and penalized regression models (such as LASSO, Ridge Regression, and Elastic Net) are simple modifications of multiple linear regression that can provide some guidance on simplifying a model through variable selection. Dimension reducing techniques, such as Partial Least Squares and Principal Components Analysis, are more complex than regression but have the ability to handle highly correlated independent variables. Each of the aforementioned techniques are valuable in predictive modeling if used properly. This paper provides a mathematical introduction to these developments in selective inference. A sample dataset is used to demonstrate modeling and interpretation. Further, the applications to big data, as well as advantages and disadvantages of each procedure, are discussed.</p>","abstract_has_math":false,"creators":["Papke, Sarah"],"institution":null,"degree_name":"MS","degree_level":"Immediate Access","degree_discipline":"Computational Mathematics","degree_department":null,"school":null,"contributors":["Frank D'Amico","John Kern","Sean Tierney"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-01-01T08:00:00Z","date_published":"2017-01-01T08:00:00Z","updated_at":"2026-07-24T02:09:13Z","subjects":["Dimension Reduction; Partial Least Squares; Penalized Regression; Predictive Modeling; Regression; Selective Inference"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://dsc.duq.edu/etd/182","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Frank D'Amico","John Kern","Sean Tierney"]},{"key":"dc:creator","label":"Author","values":["Papke, Sarah"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2017-08-10T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computational Mathematics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Immediate Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Dimension Reduction; Partial Least Squares; Penalized Regression; Predictive Modeling; Regression; Selective Inference"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://dsc.duq.edu/etd/182"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Several problems arise when attempting to use traditional predictive modeling techniques on ‘big data.’ For instance, multiple linear regression models cannot be used on datasets with hundreds of variables. However several techniques are becoming common tools for selective inference as the need for analyzing big data increases. Forward selection and penalized regression models (such as LASSO, Ridge Regression, and Elastic Net) are simple modifications of multiple linear regression that can provide some guidance on simplifying a model through variable selection. Dimension reducing techniques, such as Partial Least Squares and Principal Components Analysis, are more complex than regression but have the ability to handle highly correlated independent variables. Each of the aforementioned techniques are valuable in predictive modeling if used properly. This paper provides a mathematical introduction to these developments in selective inference. A sample dataset is used to demonstrate modeling and interpretation. Further, the applications to big data, as well as advantages and disadvantages of each procedure, are discussed.
"]},{"key":"dc:title","label":"Title","values":["A Review of 'Big Data' Variable Selection Procedures For Use in Predictive Modeling"]}]}],"canonical_facts":{"dc:contributor":["Frank D'Amico","John Kern","Sean Tierney"],"dc:creator":["Papke, Sarah"],"dc:date.available":["2017-08-10T07:00:00Z"],"dc:description.abstract":["Several problems arise when attempting to use traditional predictive modeling techniques on ‘big data.’ For instance, multiple linear regression models cannot be used on datasets with hundreds of variables. However several techniques are becoming common tools for selective inference as the need for analyzing big data increases. Forward selection and penalized regression models (such as LASSO, Ridge Regression, and Elastic Net) are simple modifications of multiple linear regression that can provide some guidance on simplifying a model through variable selection. Dimension reducing techniques, such as Partial Least Squares and Principal Components Analysis, are more complex than regression but have the ability to handle highly correlated independent variables. Each of the aforementioned techniques are valuable in predictive modeling if used properly. This paper provides a mathematical introduction to these developments in selective inference. A sample dataset is used to demonstrate modeling and interpretation. Further, the applications to big data, as well as advantages and disadvantages of each procedure, are discussed.
"],"dc:identifier":["https://dsc.duq.edu/etd/182"],"dc:language":["English"],"dc:subject":["Dimension Reduction; Partial Least Squares; Penalized Regression; Predictive Modeling; Regression; Selective Inference"],"dc:title":["A Review of 'Big Data' Variable Selection Procedures For Use in Predictive Modeling"],"thesis:degree_discipline":["Computational Mathematics"],"thesis:degree_level":["Immediate Access"],"thesis:degree_name":["MS"]},"updated_at":"2026-07-24T02:09:13Z"}