Dear Members of the Kansas-Western Missouri Chapter of the ASA,
Please join us for our spring Chapter meeting on Thursday, April 10th, 2014 at Kansas State
University. The meeting will be held in the Holiday Inn of Manhattan. A map is provided below for
your convenience. Professor Sudipto Banerjee in School of Public Health, Division of Biostatistics
at University of Minnesota will be presenting the Keynote Address “On complex spatial
dependencies: Low-rank cross-covariance models”. Professor Banerjee will also present a seminar
talk at Department of Statistics at Kansas State University on “Space, Time and Gradients: Why we
need them in statistical modeling for public health data” at 4:00pm, Dickens 207. Additional
information about his talks is listed below.
The cost to attend the chapter meeting is $30.00 ($20.00 for students), which includes dinner.
Registration for this event can be done online at
https://www.123signup.com/register?id=ddtmd
The on-line registration time frame is
Registration Starts:
Feb 06, 2014
Registration Ends:
Apr 07, 2014
Please contact our Chapter president, Weixin Yao(wxyao@ksu.edu) or Chapter secretary Juan Du
(dujuan@ksu.edu) if you have any questions.
We look forward to seeing you in Manhattan!
Agenda
6:00 – 6:15 p.m. Social time
6:15 - 6:30 p.m. The induction ceremony for Mu Sigma Rho Honor Society
6:30 – 7:15 p.m. Dinner
7:15 – 7:30 p.m. Chapter business
7:30 – 8:30 p.m. Keynote Address
Chapter Meeting Keynote Address
Title: On complex spatial dependencies: Low-rank cross-covariance models.
Abstract: Advances in geo-spatial technologies have created data-rich environments which provide
extraordinary opportunities to understand the complexity of large and spatially indexed data and
help tackle increasingly complex inferential questions in the natural sciences. A setting commonly
encountered today is where multiple geo-referenced outcomes have been monitored over different
spatial locations and inferential interest resides with how the association pattern among these
outcomes change over space. These lead to joint (multivariate) spatial process models that also
accommodate non-stationary correlations among the outcomes. Direct application of such
multivariate models to even moderate-sized spatial data, unfortunately, is computationally
prohibitive. Here, we discuss approaches that help overcome these inferential hurdles without
sacrificing richness in underlying association structures. We propose a new class of low-rank
spatially-varying cross-covariance matrices that produce non-degenerate spatial processes and that
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effectively capture non-stationary covariances among the multiple outcomes. We apply our methods
to a soil nutrients data collected at the La Selva Biological Station, Costa Rica. Here, interest lies in
visual and statistical inference in the spatially-varying relationship among the underlying spatial
processes that generate the outcomes. Our framework produces substantive inferential tools such as
maps of non-stationary cross-covariances that have hitherto not been easily available for
environmental scientists and researchers.
*** This is joint work with Rajarshi Guhaniyogi, Andrew Finley and Richard Kobe. ***.
Additional Information
Additional statistical talk will be given by Professor Sudipto Banerjee at Department of Statistics,
Kansas State University on April 10th from 4:00 – 5:00 p.m. The title of his talk is “Space, Time and
Gradients: Why we need them in statistical modeling for public health data”.
Abstract: Advances in Geographical Information Systems (GIS) and related software have led to a
burgeoning of spatial-temporal databases. Statisticians and spatial analysts today routinely encounter
situations where they seek to model relationships among variables across space and time. In recent
times interest has turned to inferring about rates of change of health outcomes over space and time.
Why are such questions relevant and how should we estimate them? One example considers
analyzing monthly hospitalization rates aggregated over the counties in California where hospital
management seeks to carry out inference on gradients of the temporal process, while at the same
time accounting for spatial similarities across neighboring regions. Another example (an extension) is
to analyze spatial-temporal gradients for environmental pollutants to understand the nature of
dispersal of pollutants. Here, we are interested in directional rates of change over space at any given
time, temporal gradients at any given location and even "mixed" gradients, e.g., how the temporal
rate of change varies over space. We will work within a fully Bayesian inferential paradigm without
unnecessary, and potentially inflexible, parametric modeling assumptions and obtain the full
posterior predictive distribution for these gradients using process-based models.
*** This is joint work with Harrison Quick and Bradley P. Carlin. ***
Key words: Bayesian inference, Gradients, Markov random fields, Smoothness of spatial processes;
Spatial-temporal processes.
Biographical sketch of Professor Sudipto Banerjee:
Dr. Sudipto Banerjee is a Professor in Division of Biostatistics at University of Minnesota.
Dr. Sudipto Banerjee’s research, dissertation advising and mentoring activities focus upon statistical
modeling and analysis of geographically referenced datasets, Bayesian statistics, interface between
statistics and Geographical Information Systems, and statistical computing. He has published over
ninety peer-reviewed journal articles, several book chapters and has co-authored a book titled
"Hierarchical Modeling and Analysis for Spatial Data". In 2009 he was honored with the Abdel El
Sharaawi Award from the International Environmetrics Society. In 2011 he was honored with the
Mortimer Spiegelman Award from the American Association of Public Health. This award, given
annually since 1970, is presented to the nation's most outstanding public health statistician under the
age of 40.
http://sph.umn.edu/faculty1/expertise/spatial-statistics/name/sudipto-banerjee/
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Chapter Meeting Address:
Landon Room
Holiday Inn Manhattan at Campus
(The intersection of N17th Street and Anderson Avenue)
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