Applied Statistics - Ben Goodrich 

 The applied statistics workshop begins this Wednesday (9/19) at 1200pm in N-354.  The applied stats workshop is billed as a tour of the applied statistics community at Harvard University, with scholars from Economics, Political Science, Public Health, Sociology, Statistics, and other fields coming together to present cutting edge research.  We are happy to have Ben Goodrich (Government G-5) presenting his work on Semi-Exploratory Factor Analysis.  Below is a summary of his talk:  

 I develop a new estimator called semi-exploratory factor analysis (SEFA) that is slightly more restrictive than exploratory factor analysis (EFA) and considerably less restrictive than confirmatory factor analysis.  SEFA has three main advantages over EFA: the objective function has a unique global optimum, rotation is unnecessary, and hypotheses about models can easily be tested. SEFA represents a very difficult constrained optimization problem with nonlinear inequality constraints that, for all practical purposes, can only be solved with a genetic optimization algorithm, such as RGENOUD ( Mebane and Sekhon 2007 ). This use of new features of RGENOUD is potentially fruitful for difficult optimization problems besides those in factor analysis. 

 We have a preliminary schedule posted on the  course website ; please contact me (Justin Grimmer, jgrimmer@fas.harvard.edu) if you are interested in presenting in one of our few remaining open spots.  And of course, a light lunch will be provided.