Hi all,
We hope that you can join us for the final Applied Statistics Workshop
of the year this Wednesday, April 27th when we will be happy to have
Benjamin Lauderdale, currently a College Fellow in the Department of
Government, Harvard University and soon to be at the London School of
Economics. You will find an abstract below. As always, we will serve a
light lunch and the talk will begin around 12:15p.
“There Are Many Median Justices on the Supreme Court”
Benjamin Lauderdale
Department of Government, Harvard University
CGIS K354 (1737 Cambridge St.)
Wednesday, April 27th, 2011 12 noon
Abstract:
While unidimensional preference estimates for the U.S. Supreme Court
exist in both constant and time-varying forms, estimating variation in
preferences across areas of the law has been difficult because
multidimensional scaling models perform poorly with only nine voters.
We introduce a new approach to recovering estimates of judicial
preferences that are localized to particular legal issues as well as
periods of time. Using expert issue area codes and majority opinion
citations to identify the strength of substantive relationships
between cases, we apply a kernel-weighted optimal classification
estimator to analyze how justices' preference vary across both areas
of the law and time. Allowing for issue-variation in preferences
improves the predictive power of estimated preference orderings more
than allowing for time-variation. We find substantial variation in
the identity of the median justice across areas of the law during most
periods of the modern court, suggesting a need to reconsider empirical
and theoretical research that hinges on the existence of a unitary and
well-identified median justice.
Cheers,
matt.
~~~~~~~~~~~
Matthew Blackwell
PhD Candidate
Institute for Quantitative Social Science
Department of Government
Harvard University
url: http://people.fas.harvard.edu/~blackwel/
Hi all,
We hope that you can join us for the penultimate Applied Statistics
Workshop of the year this Wednesday, April 20th. This week we are
extremely excited to have Jeffrey Lewis, Associate Professor of
Political Science at UCLA, presenting on the compactness of
congressional districts, a topic that involves some interesting
econometric issues as well as a large GiS component. Note that this is
a change from what is on the schedule. As usual, we will start the
workshop at 12 noon with a light lunch and begin the talk at 12:15. We
wrap up the workshop at 1:30pm.
"A study of Congressional district compactness, 1789-2011"
Jeffrey B. Lewis
Department of Political Science, UCLA
CGIS K354 (1737 Cambridge St)
Wednesday, April 20th, 12 noon.
Cheers,
matt.
~~~~~~~~~~~
Matthew Blackwell
PhD Candidate
Institute for Quantitative Social Science
Department of Government
Harvard University
url: http://people.fas.harvard.edu/~blackwel/
Hi all,
We hope that you can join us for the Applied Statistics Workshop this
Wednesday, April 13th, 2011 when we will be happy to finally have
Patrick Perry from the Statistics and Information Sciences Laboratory.
This is a talk rescheduled from earlier in the term when the weather
was much worse. You will find an abstract and paper link below. As
always, we will serve a light lunch and the talk will begin around
12:15p.
“Point process modeling for directed interaction networks”
Patrick Perry
Statistics and Information Sciences Laboratory
CGIS K354 (1737 Cambridge St.)
Wednesday, April 13th, 2011 12 noon
Abstract:
Network data often take the form of repeated interactions between
senders and receivers tabulated over time. Rather than reducing these
data to binary ties, a model is introduced for treating directed
interactions as a multivariate point process: a Cox multiplicative
intensity model using covariates that depend on the history of the
process. Consistency and asymptotic normality are proved for the
resulting partial-likelihood-based estimators under suitable
regularity conditions, and an efficient fitting procedure is
described. Multicast interactions--those involving a single sender but
multiple receivers--are treated explicitly. A motivating data example
shows the effects of reciprocation and group-level preferences on
message sending behavior in a corporate e-mail network.
Paper: http://arxiv.org/abs/1011.1703
Cheers,
matt.
~~~~~~~~~~~
Matthew Blackwell
PhD Candidate
Institute for Quantitative Social Science
Department of Government
Harvard University
url: http://people.fas.harvard.edu/~blackwel/
Hi all,
We are really excited about this week's Applied Statistics Workshop
this Wednesday, April 4th, 2011 when we will be happy to have Kaisey
Mandel from the Harvard-Smithsonian Center for Astrophysics. Kaisey
will be presenting on hierarchical Bayesian models in Astrophysics.
This will be a great chance to see how the statistical methods that we
use transport to other disciplines around the sciences. No prior
knowledge of astrophysics required! You will find an abstract and a
link to a paper below. As always, we will serve a light lunch and the
talk will begin around 12:15p.
“Hierarchical Bayesian Models for Type Ia Supernova Light Curves,
Dust, and Cosmic Distances”
Kaisey Mandel
Harvard-Smithsonian Center for Astrophysics
CGIS K354 (1737 Cambridge St.)
Wednesday, April 4th, 2011 12 noon
Abstract:
Type Ia supernovae (SN Ia) are the most precise cosmological distance
indicators and are important for measuring the acceleration of the
Universe and the properties of dark energy. To obtain the best
distance estimates, the photometric time series (apparent light
curves) of SN Ia at multiple wavelengths must be properly modeled. The
observed data result from multiple random and uncertain effects, such
as measurement error, host galaxy dust extinction and reddening,
peculiar velocities, and distances. Furthermore, the intrinsic,
absolute light curves of SN Ia differ between individual events:
different SN Ia have different intrinsic luminosities, colors and
light curve shapes, and these properties are correlated in the
population. A hierarchical Bayesian model provides a natural
statistical framework for coherently accounting for these multiple
random effects while fitting individual SN Ia and the population
distribution. I will discuss the application of this statistical model
to optical and near-infrared data for computing inferences about the
dust, distances and intrinsic covariance structure of SN Ia. Using
this model, I demonstrate that the combination of optical and NIR data
improves the precision of SN Ia distance predictions by about a factor
of 2 compared to using optical data alone. Finally, I will discuss
some open research problems concerning statistical analysis of
supernova data and their application to cosmology.
Paper: http://arxiv.org/abs/1011.5910
Cheers,
matt.
~~~~~~~~~~~
Matthew Blackwell
PhD Candidate
Institute for Quantitative Social Science
Department of Government
Harvard University
url: http://people.fas.harvard.edu/~blackwel/
