Bayesian variable selection with spike-and-slab priors
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The Ohio State University / OhioLINK
2016
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| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=osu1461940937 |
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ndltd-OhioLink-oai-etd.ohiolink.edu-osu14619409372021-08-03T06:36:25Z Bayesian variable selection with spike-and-slab priors Agarwal, Anjali Statistics Psychology A major focus of intensive methodological research in recent times has been on knowledge extraction from high-dimensional datasets made available by advances in research technologies. Coupled with the growing popularity of Bayesian methods in statistical analysis, a range of new techniques have evolved that allow innovative model-building and inference in high-dimensional settings – an important one among these being Bayesian variable selection (BVS). The broad goal of this thesis is to explore different BVS methods and demonstrate their application in high-dimensional psychological data analysis. In particular, the focus will be on a class of sparsity-enforcing priors called `spike-and-slab’ priors which are mixture priors on regression coefficients with density functions that are peaked at zero (the `spike’) and also have large probability mass for a wide range of non-zero values (the `slab’). It is demonstrated that BVS with spike-and-slab priors achieved a reasonable degree of dimensionality-reduction when applied to a psychiatric dataset in a logistic regression setup. BVS performance was also compared to that of LASSO (least absolute shrinkage and selection operator), a popular machine-learning technique, as reported in Ahn et al.(2016). The findings indicate that BVS with a spike-and-slab prior provides a competitive alternative to machine-learning methods, with the additional advantages of ease of interpretation and potential to handle more complex models. In conclusion, this thesis serves to add a new cutting-edge technique to the lab’s tool-shed and helps introduce Bayesian variable-selection to researchers in Cognitive Psychology where it still remains relatively unexplored as a dimensionality-reduction tool. 2016-09-29 English text The Ohio State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=osu1461940937 http://rave.ohiolink.edu/etdc/view?acc_num=osu1461940937 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |
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NDLTD |
| language |
English |
| sources |
NDLTD |
| topic |
Statistics Psychology |
| spellingShingle |
Statistics Psychology Agarwal, Anjali Bayesian variable selection with spike-and-slab priors |
| author |
Agarwal, Anjali |
| author_facet |
Agarwal, Anjali |
| author_sort |
Agarwal, Anjali |
| title |
Bayesian variable selection with spike-and-slab priors |
| title_short |
Bayesian variable selection with spike-and-slab priors |
| title_full |
Bayesian variable selection with spike-and-slab priors |
| title_fullStr |
Bayesian variable selection with spike-and-slab priors |
| title_full_unstemmed |
Bayesian variable selection with spike-and-slab priors |
| title_sort |
bayesian variable selection with spike-and-slab priors |
| publisher |
The Ohio State University / OhioLINK |
| publishDate |
2016 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=osu1461940937 |
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AT agarwalanjali bayesianvariableselectionwithspikeandslabpriors |
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