Department of
Mathematical Sciences University of
Nevada, Las Vegas
2008-2009 [2006-2008] [2009-2010] [2010-2011] For more information, contact the Colloquium/Seminar
Coordinator, Dr. Hokwon Cho (To see Math Dept Colloquia/Seminars, click next: Math Dept Seminar) |
Fall 2008 |
·
Friday
3:30 p.m. September 12, CBC C-224: Dr. Ashis SenGupta Department
of Statistics, University of California, Riverside and Applied
Statistics Unit, Indian Statistical Institute, Kolkata, India Abstract: Observations
on angular propagations, directional orientations, and even strictly periodic
phenomena can be cast in the arena of directional data (DD). Such
observations are frequently encountered in almost every sphere of applied
science, ranging from e.g. agriculture to zoology, chronotherapy
to defence, etc. There has been a paucity of probability
distributions to model DD, even on 3-smooth manifolds, e.g. torus, cylinder,
and hence on their higher dimensional generalizations. We present here
unified approaches to derivations of such distributions. Tests for orthogonality of the directional random variables are
then obtained based on these distributions. Next, models for regression with
linear and circular variables are presented and related inference procedures
are developed. Both classical and Bayesian approaches are discussed. Finally,
the proposed methods are illustrated by several real-life examples. ·
Friday
11:30 a.m. October 3, CBC C-224: Dr. Ben Kedem Department
of Statistics, Title: Bayesian
Spatial Prediction Abstract: We discuss Bayesian
spatial/temporal prediction in transformed Gaussian random fields where the
transformation belongs to a parametric family. ·
Friday
11:30 a.m. October 10, CBC C-224: Dr. Lurdes Inoue Department
of Biostatistics, Abstract: In this talk we discuss some modeling approaches to
investigate disease progression. First, we propose a model that links
longitudinal biomarker and disease progression. Specifically, we consider an
underlying latent disease process that describes the onset of the disease and
models the transition to an advanced stage of the disease as dependent on the
biomarker levels. Next, we propose a variation of the above model to
investigate disease progression using data prospectively collected in a
screening study. We illustrate our methods through simulations and a case
study in prostate cancer.
·
Friday
11:30 a.m. November 7, CBC C-224: Dr. Glen Meeden Department
of Statistics, Title: A Noninformative Bayesian Approach to Finite Population Sampling
Using Auxiliary Variables Abstract: In finite population
sampling prior information is often available in the form of partial
knowledge about an auxiliary variable, for example its mean may be known. In
such cases, the ratio estimator and the regression estimator are often used
for estimating the population mean of the characteristic of interest. The Polya posterior has been developed as a noninformative Bayesian approach to survey sampling. It
is appropriate when little or no prior information about the population is
available. Here we show that it can be extended to incorporate types of
partial prior information about auxiliary variables. We will see that it
typically yields procedures with good frequentist
properties even in some problems where standard frequentist
methods are difficult to apply. Moreover one does not need to select a model
which explictly relates the characteristic of
interest to the auxiliary variables. ·
Friday
11:30 a.m. November 21, CBC C-224: Dr.
N. Balakrishnan Department
of Statistics, Title: Over/Under-Dispersed
Poisson Distributions and Processes Abstract: In this talk, I will establish several connections of the Poisson weight function to overdispersion and underdispersion. Specifically, I will show that the logconvexity (logconcavity) of the mean weight function is a necessary and sufficient condition for overdispersion (underdispersion) when the Poisson weight function does not depend on the original Poisson parameter. I will also discuss some properties of the weighted Poisson distributions (WPDs). I will then introduce a notion of pointwise duality between two WPDs and discuss some associated properties. Next, after presenting some illustrative examples and providing a discussion on various Poisson weight functions used in practice, I will make some concluding remarks. Finally, I will use these results to introduce and discuss over/under-dispersed Poisson processes. |
Spring
2009 |
·
Fri.
2:30 p.m. January 23, CBC C-224:
Dr.
Abel Rodriguez Department of Applied Mathematics and Statistics, Abstract: This talk discusses
clustering procedures for nested samples of curves, where multiple profiles
are collected for each subject in the study. We start by considering the
application of standard functional clustering tools to this
problems, which lead to groupings based on the average profile for
each subject. After discussing some of the shortcoming of this
approach, we present a model based on a generalization of the nested
Dirichlet processes that uses the information on the distribution of curves
to generate the clusters. The method is illustrated using data from the
Early Pregnancy Study on hormone profiles along multiple menstrual periods
for a cohort of women. The resulting model simultaneous cluster both
curves and subjects, allowing us to identify outlier curves for each group of
women, as well as outlying women whose distribution of profiles differs from
the rest. ·
Fri.
1:00 p.m. February 6, CBC C-224:
Dr. Grace Chiu Department of Statistics and
Actuarial Science, Abstract: We propose a model-based approach for constructing
ecological health indices through statistical inference. Our latent health
factor index (LHFI) is obtained by estimating an unobservable health factor
term in a mixed-effects ANOCOVA that directly models the relationship among
indicator variables (or metrics) and health. Unlike conventional indices (e.g. IBI and
O/E index) that rely on domain-specific calibrations of metrics against
reference conditions whose non-constancy is largely unaccounted for, our
methodology (a) involves no explicit reference conditions while metrics are
intrinsically "calibrated" in the context of multiple comparisons,
and (b) can naturally incorporate spatio-temporal
influences on calibration schemes. ·
Fri.
11:30 a.m. February 13, CBC C-224:
Dr. Barry Arnold Department of Statistics, Abstract: The Azzalini skew-normal
density of the form 2φ(x)Φ(λx) can be
viewed as having arisen by considering a bivariate
random variable (X,Y) with a classical bivariate
normal density and focussing on the conditional
distribution of X given Y < E(Y). The same family of distributions is
encountered if we consider the conditional distribution of X given Y >
E(Y). A slightly more general family is provided by considering the
conditional distribution of X given Y > y_{0} where y_{0}
is not necessarily equal to E(Y). The resulting model (which we can call a
hidden truncation model, since we only observe X if the unobserved or hidden
variable Y exceeds a threshold value, is a flexible extension of the
classical univariate normal model with potential to
.t a broad spectrum of data configurations which may not be well fitted by a
classical normal model. In the present paper we consider several other basic bivariate non-Gaussian models and investigate the nature
of their corresponding hidden truncation models. In particular, it is of
interest to identify situations in which hidden truncation fails to augment
the basic model. Additive component representations provide an alternative to
the hidden truncation paradigm in the normal case. It is conjectured that it
is only in the normal case that the two models coincide. ·
Fri.
11:30 a.m. February 27, CBC C-224:
Dr. Kevin Quinn Department
of Government & Institute for Quantitative Social Science, Abstract: We amass a new,
large-scale dataset of newspaper editorials that allows us to calculate
fine-grained measures of the political positions of newspaper editorial
pages. Collecting and classifying over 1500 editorials adopted by 25 major US
newspapers on 495 Supreme Court cases from 1994 to 2004, we apply an item
response theoretic approach to place newspaper editorial boards on a
substantively meaningful—and long validated—scale of political preferences.
We validate the measures, show how they can be used to shed light on the
permeability of the wall between news and editorial desks, and argue that the
general strategy we employ has great potential for more widespread use. ·
Fri.
11:30 a.m. April 24, CBC C-224:
Dr. Charles Davis President,
Environmetrics and Statistics Ltd Abstract: Lognormal (LN)
distributions are often assumed for environmental contaminants, with perhaps
some justification. But decisions are made from measurements, not the
unobservable concentrations themselves. These often do not have LN
distributions. Rather, at fixed concentrations distributions of measurements
are often normally distributed, and if low-level measurements are unbiased
one has negative values; standard LN inference techniques fail in this
setting. This reality is universally ignored; measurement values are censored
at a Reporting Limit, the negative values are never seen, and we continue to
develop (and publish) methods for left-censored LN environmental data. |