Regularization in Mediation Models: A Monte Carlo Simulation Comparing Different Regularization Penalties in Multiple Mediation Models

Simple item page
dc.contributor.advisorChoi, Ji Yeh
dc.contributor.authorSingh, Arjunvir
dc.date.accessioned2022-12-14T16:37:06Z
dc.date.available2022-12-14T16:37:06Z
dc.date.copyright2022-08-17
dc.date.issued2022-12-14
dc.date.updated2022-12-14T16:37:06Z
dc.degree.disciplinePsychology (Functional Area: Quantitative Methods)
dc.degree.levelMaster's
dc.degree.nameMA - Master of Arts
dc.description.abstractThe two fundamental goals in statistical learning are establishing prediction accuracy and discovering the correct set of predictors to ensure model specificity. Although the field of variable selection has made significant strides over the past decades, these methods are yet to be fully adapted to mediation models. Regularization methods that utilize the l1 penalty such as the Lasso and adaptive Lasso incorporate a small amount of controlled bias into the ordinary least squares estimates to help improve the generalizability of the estimates by significantly reducing their variance across samples. Additionally, the Lasso can perform variable selection and help achieve model selection consistency or sparsistency. Recent literature has proposed methods that have introduced regularization to mediation models. These include regularized structural equation modelling or RegSEM. The current research compares the performance of various regularization penalties such as the Lasso, adaptive Lasso, MCP and SCAD in the context of mediation models. No single regularization penalty performed optimally across all simulation conditions. Additionally, we observed disproportionate selection rates for the Lasso and SCAD penalty with alternating mediators which was indicative of disproportionate shrinkage of the a and b pathways. However, the absolute bias induced in the a and b pathways was equivalent across all samples for each penalty term. This highlights the perils of shrinking individual regression pathways instead of indirect effects as a whole. Overall, the choice of the type of regularization penalty implemented depends on the particularities of the research question.
dc.identifier.urihttp://hdl.handle.net/10315/40732
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectPsychology
dc.subjectStatistics
dc.subjectQuantitative psychology
dc.subject.keywordsl1-penalty
dc.subject.keywordsRegularization
dc.subject.keywordsBias-variance trade-off
dc.subject.keywordsSparsity
dc.subject.keywordsVariable selection
dc.subject.keywordsMediation
dc.subject.keywordsLasso
dc.subject.keywordsSCAD
dc.subject.keywordsMCP
dc.subject.keywordsAdaptive lasso
dc.titleRegularization in Mediation Models: A Monte Carlo Simulation Comparing Different Regularization Penalties in Multiple Mediation Models
dc.typeElectronic Thesis or Dissertation

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
_Singh_Arjunvir_2022_Masters.pdf
Size:
1 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
license.txt
Size:
1.87 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
YorkU_ETDlicense.txt
Size:
3.39 KB
Format:
Plain Text
Description:
Download

Collections

Psychology (Functional Area: Quantitative Methods)

All items in the YorkSpace institutional repository are protected by copyright, with all rights reserved except where explicitly noted.