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(To
see all colloquia/seminars, click next: Math
Dept Seminar) For more information,
contact the Seminar Organizer, Dr.
Hokwon Cho |
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Spring
2008 |
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·
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. |
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Fall 2007 |
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·
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. |
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Fall 2006 |
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·
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. |
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