Seminar Events 2008-2009
Co-sponsored with AMSC Program
Applied Statistics
SPEAKER: Miscellaneous Faculty from: MATH,
ENEE, CMSC, JPSM, BMGT, ANSC, ISR, UMIACS, ,
TITLE:
10-Minute Applied Statistics Madness
TIME AND PLACE:
Tuesday, May 5, 2009, 3:30pm-5:30pm
Colloquium Room 3206, Math Building
This Event consists of 10-minute presentations of individual
faculty members' applied statistical research together with briefer
overviews of applied statistical research of colleagues in their
same academic units. See flyer here
for more information.
The talks will be followed by a
Reception with food from 5:30-6:30pm in MTH 3206, the Math
Department Lounge.
Co-sponsored with Statistics
Seminar, Mathematics Department
SPEAKER: Prof. Edward J. Wegman,
George Mason University
TITLE:
Mixture Models for Document Clustering
TIME AND PLACE:
Thursday, October 30, 2008, 3:30pm
Colloquium Room 3206, Math Building
ABSTRACT:
Automatic clustering and classification of documents within corpora is
a challenging task. Often, comparing word usage within the corpus, the
so-called bag-of-words methodology, does this. The lexicon for a corpus
can indeed be very large. For the example of 503 documents that we
consider, there are more than 7000 distinct terms and more than 91,000
bigrams. This means that a term vector characterizing a document will
be approximately 7000 dimensional. In this talk, we use an adaptation
of normal mixture models with 7000 dimensional data to locate centroids
of clusters. The algorithm works surprisingly well and is linear in all
the size metrics.
PowerPoint slides for the
talk can be found here.
Immediately following the talk, there
will be a Reception and High Tea in the Mathematics Department Lounge,
MTH 3201.
Co-sponsored with Statistics
Seminar, Mathematics Department
SPEAKER: Prof. Gauri S. Datta,
University of Georgia, Department of Statistics
TITLE:
Estimation of Small Area Means under Measurement Error Models
TIME AND PLACE:
Tuesday, November 18, 2008, 3:30pm
Room 1313, Math Building
ABSTRACT:
In recent years demand for reliable estimates for characteristics of
small domains (small areas) has greatly increased worldwide due to
growing use of such estimates in formulating policies and programs,
allocating government funds, planning regional development, and
marketing decisions at local level. However, due to cost and
operational considerations, it is seldom possible to procure a large
enough overall sample size to support direct estimates of adequate
precision for all domains of interest. It is often necessary to
employ indirect estimates for small areas that can increase the
effective domain sample size by borrowing strength from related areas
through linking models, using census and administrative data and other
auxiliary data associated with the small areas. To this end, the
nested error regression model for unit-level data and the Fay-Herriot
model for the area-level data have been widely used in small area
estimation. These models usually treat that the explanatory variables
are measured without error. However, explanatory variables are often
subject to measurement error. Both functional and structural
measurement error models have been recently proposed by researchers in
small area estimation to deal with this issue. In this talk, we
consider both functional and structural measurement error models in
discussing empirical Bayes (equivalently, empirical BLUP) estimation
of small area means.
Immediately following the talk, there will be a Reception and High
Tea in the Mathematics Department Lounge,MTH 3201.
DISTINGUISHED STATISTICS CONSORTIUM LECTURE
SPEAKER: Mitchell H. Gail, M.D., Ph.D.
Senior Investigator, Biostatistics Branch, Div. Cancer
Epidemiology & Genetics, National Cancer Institute
TITLE:
Absolute Risk: Clinical Applications and Controversies
TIME: Friday, December 5, 2008, 3:15pm
PLACE: TBA
ABSTRACT: Absolute risk is the probability that a disease will develop in a defined age interval in a person with specific risk factors. Sometimes
absolute risk is called "crude" risk to distinguish it from the
cumulative "pure" risk that might arise in the absence of competing
causes of mortality. After defining absolute risk, I shall present a
model for absolute breast cancer risk and illustrate its clinical
applications. I will also describe the kinds of data and approaches
that are used to estimate models of absolute risk and two criteria,
calibration and discriminatory accuracy, that are used to evaluate
absolute risk models. In particular, I will address whether well
calibrated models with limited discriminatory accuracy can be useful.
Immediately following the talk there
will be a formal Discussion, with a Reception to follow that.
Details concerning the Discussant and the location of the Event
will be posted here in the near future.
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