Department of
Mathematical Sciences University of
Nevada, Las Vegas
2006-2008 [2008-2009] [2009-2010] [2010-2011]
[Current
Year] For more information, contact the Colloquium/Seminar
Coordinator, Dr. Hokwon Cho (To see Math Dept Colloquia/Seminars, click next: Math Dept Seminar) |
Fall 2006 |
·
Fri.
11:00 a.m. November 3, CBC C-225:
Dr. Nitis Mukhopadhyay Department of Statistics, Abstract: A horticulturist was considering the number of days
each marigold variety took from planting seeds to reach a stage when first bud
appeared. The primary interest was to estimate the maximum waiting time
between “seeding” and “first budding” among three varieties. It was thought
that a 99% confidence interval of width one day would suffice since
the data could be recorded with accuracy of one-half day. We assumed a normal
distribution for the response variable. The horticulturist provided positive
lower bounds for the variances that led to unequal pilot sample sizes.
Accordingly, a new two-stage sampling design had to be developed and
implemented. We will show that the data validated all assumptions made during
the course of this investigation. Some
of the important exact as well as large-sample properties of the proposed
methodology will also be summarized. Interpretations of the properties would
be highlighted with real data.
Finally, we will argue that the new methodology is theoretically
superior to an existing methodology in case the pilot sizes could somehow be
“chosen” equal. Using the data on hand, the superiority of the new
methodology will be indicated. |
Fall 2007 |
·
Fri.
11:00 a.m. October 12, CBC C-225:
Dr. Junyong Park Department of Mathematics and Statistics, Abstract: We consider the problem of testing whether a high
dimensional observation vector has signal, i.e., testing all the mean values
are zero versus the alternative that non-zero means exist. The setup is when
the dimension of vector is large, and the mean
vector is 'sparse', e.g., the small fraction of mean values is non-zero. We
suggest a test which is not sensitive to the exact tail behavior under
normality assumption. In particular, if the 'moderate deviation' tail of the
distribution is represented as the product of a tail of a standard normal and
a `slowly changing' function, our suggested test is robust. In particular, a
need for robust test is expected when the observations are of the normalized
form where normality assumption is commonly used from C.L.T. ·
Fri.
11:30 a.m. November 30, CBC C-225: Dr.
Anton Westveld Department of Mathematical Sciences, Abstract: An extensive literature in international and
comparative political economy has focused on the how the mobility of capital affects
the ability of governments to tax and regulate firms. The conventional
wisdom holds that governments are in competition with each other to attract
foreign direct investment (FDI). Nation-states observe the fiscal and
regulatory decisions of competitor governments, and are forced to either
respond with policy changes or risk losing foreign direct investment, along
with the politically salient jobs that come with these investments. The
political economy of FDI suggests a network of investments with complicated
dependencies. We
propose an empirical strategy for modeling investment patterns in 24 advanced
industrialized countries from 1985-2000. Using bilateral FDI data we
estimate how increases in flows of FDI affect the flows of FDI in other
countries. Our statistical model is based on the methodology developed
by Westveld & Hoff (2007). The model
allows the temporal examination of each notion's activity level in investing,
attractiveness to investors, and reciprocity between pairs of nations.
We extend the model by treating the reported inflow and outflow data as
independent replicates of the true value and allowing for a mixture model for
the fixed effects portion of the network model. Using a fully Bayesian
approach, we also impute missing data within the MCMC algorithm used to fit
the model. A working paper can be found at: http://faculty.unlv.edu/westveld/Papers/FDI.pdf. |
Spring
2008 |
·
Friday
2:00 p.m. February 1, CBC C-224: Toby
White, Ph.D candidate Department of Statistics, Abstract: Latent class transition models are used to partition
a population into a small number of relatively homogeneous subgroups so that the movement of individuals among these
subgroups can be followed through time. One context for these
models involves the ·
Friday
11:30 a.m. March 28, CBC C-224: Dr. Yuedong Wang Department of Statistics and Applied Probability, Abstract: Almost all of the current nonparametric regression
methods such as smoothing splines, generalized
additive models and varying coefficients models assume a linear relationship
when nonparametric functions are regarded as parameters. In this talk we
present a general class of nonlinear nonparametric models that allow
nonparametric functions to act nonlinearly. They
arise in many fields as either theoretical or empirical models. We
propose new estimation methods based on an extension of the Gauss-Newton
method to infinite dimensional spaces and the backfitting
procedure. We extend the generalized cross validation and the generalized
maximum likelihood methods to estimate smoothing parameters. Connections
between nonlinear nonparametric models and nonlinear mixed effects models are
established. Approximate Bayesian confidence intervals are derived for
inference. We will also present a user friendly R function for fitting these
models. The methods will be illustrated using two real data examples. ·
Friday
11:30 a.m. May 2, CBC C-224: Dr. Kaushik Ghosh Department of Mathematical Sciences, Abstract: In longitudinal studies of
patients with the Human Immunodeficiency Virus (HIV), objectives of interest
often include modeling of individual-level trajectories of HIV Ribonucleic
Acid (RNA) as a function of time. Empirical evidence suggests that individual
trajectories often possess multiple points of rapid change, which may vary
from subject to subject --- both in number and in location. Presence of such changepoints make
the modeling of individual viral RNA levels difficult, since usual methods
become unsuitable. In this
talk, we present a new robust multiple-change point model for longitudinal
trajectories. The proposed method uses a joint model to incorporate
information from the longitudinal data as well as from informative dropouts,
which are common in such studies. A Dirichlet process prior is used to model
the distribution of the changepoints. The Dirichlet
process leads to a natural clustering, and thus, sharing of information among
subjects with similar trajectories. A fully Bayesian approach for model
fitting and prediction is implemented using the Gibbs sampler on the ACTG 398
clinical trial data. |