Dear Applied Stats Workshop members,
Please join us this Wednesday (9/26) when David Lazer, Associate Professor
of Public Policy and Director of the Program on Networked Governance at the
Kennedy School of Government, will present "Life in the Network: The Coming
Era of Computational Social Science". David provided the following summary
of his talk:
An increasing fraction of human behavior (especially relational behavior)
leaves substantial digital traces-- whether in the form of phone logs,
e-mail, instant messaging, etc. Further, increased computational power
allows the analysis of these digital traces-- e.g., through natural language
processing, statistical analysis of massive (millions of individuals)
longitudinal data, etc. These two points suggest that we are on the
precipice of dramatic new insights into collective human behavior. I will
discuss the potential future of a "computational social science", with
reference to four ongoing research projects.
As always, our workshop begins at 12 noon in CGIS-Knafel room N-354. And a
free lunch will be provided.
Best,
Justin Grimmer
Dear Applied Statistics Community,
The applied statistics workshop begins this Wednesday (9/19) at 1200pm in
N-354. This 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. For our first talk, Ben
Goodrich (Government G-5) will present his work on Semi-Exploratory Factor
Analysis. Below is a summary of his presentation:
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
[http://sekhon.berkeley.edu/papers/rgenoudJSS.pdf]<http://sekhon.berkeley.edu/papers/rgenoudJSS.pdf%5D>).
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<http://my.harvard.edu/icb/icb.do?keyword=k19231&pageid=icb.page101560>and
please contact me (Justin Grimmer,
jgrimmer(a)fas.harvard.edu) if you are interested in presenting in one of our
few remaining open spots. A light lunch will be provided and we encourage
you to invite as many of your colleagues as possible.
Best,
Justin Grimmer
