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

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