[gov3009-l] Applied Statistics - 10/7 - Marc Ratkovic & Dustin Tingley

Aaron Kaufman aaronkaufman at fas.harvard.edu
Mon Oct 5 10:12:16 EDT 2015


Hi everyone!

This week at the Applied Statistics Workshop we will be welcoming *Marc
Ratkovic *and *Dustin Tingley*. Marc is an Assistant Professor of Politics
at Princeton University, and Dustin is a Professor of Government at Harvard
University. They will be presenting work entitled *Sparse Estimation and
Uncertainty with Application to Subgroup Analysis*.  Please find the
abstract below and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations/peng-ding-harvard-sensitivity-analysis-without>.
Additionally, the paper is available here
<http://scholar.harvard.edu/files/dtingley/files/sparsereg.pdf>.

As usual, we will meet in CGIS Knafel Room 354 from noon to 1:30pm, and
lunch will be provided.  See you all there! To view previous Applied
Statistics presentations, please visit the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/videos>.

-- Aaron Kaufman

Title: Sparse Estimation and Uncertainty with Application to Subgroup
Analysis

Abstract: We introduce a Bayesian method, LASSOplus, that unifies recent
contributions in the sparse modeling literatures, while substantially
extending upon pre-existing estimators in terms of both performance and
flexibility. Unlike existing Bayesian variable selection methods, LASSOplus
both selects and estimates effects, while returning estimated confidence
intervals among discovered effects. Furthermore, we show how LASSOplus
easily extends to modeling repeated observations, and permits a simple
Bonferroni correction to control coverage on confidence intervals among
discovered effects. We situate the LASSOplus in the literature on exploring
sub-group effects, a topic that often leads to a proliferation of
estimation parameters. We also offer a simple pre-processing step that
draws on recent theoretical work to estimate higher-order effects that can
be interpreted independent of their lower-order terms. A simulation study
illustrates the method’s performance relative to several existing variable
selection methods. Application to an existing study of support for climate
treaties illustrates the method’s ability to discover substantively
relevant effects. Software implementing the method is made publicly
available in the R package *sparsereg*.
-- 
Aaron R Kaufman
PhD Candidate, Harvard University
Department of Government
818.263.5583
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