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A Review of 'Big Data' Variable Selection Procedures for Use in Predictive Modeling.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	A Review of 'Big Data' Variable Selection Procedures for Use in Predictive Modeling./
Author:	Papke, Sarah.
Description:	1 online resource (69 pages)
Notes:	Source: Masters Abstracts International, Volume: 56-06.
Contained By:	Masters Abstracts International56-06(E).
Subject:	Statistics. -
Online resource:	click for full text (PQDT)
ISBN:	9780355220292

A Review of 'Big Data' Variable Selection Procedures for Use in Predictive Modeling.
Papke, Sarah.

A Review of 'Big Data' Variable Selection Procedures for Use in Predictive Modeling. - 1 online resource (69 pages)

Source: Masters Abstracts International, Volume: 56-06.

Thesis (M.S.)

Includes bibliographical references

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 modi?cations 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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355220292Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

A Review of 'Big Data' Variable Selection Procedures for Use in Predictive Modeling.
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500 $a Source: Masters Abstracts International, Volume: 56-06.
500 $a Adviser: Frank D'Amico.
502 $a Thesis (M.S.) $c Duquesne University $d 2017.
504 $a Includes bibliographical references
520 $a 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 modi?cations 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
650 4 $a Mathematics. $3 527692
650 4 $a Information science. $3 561178
655 7 $a Electronic books. $2 local $3 554714
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