Department of
Mathematical Sciences University of
Nevada, Las Vegas
20122013 [20062008] [20082009] [20092010] [20102011] [20112012] [20132014]
For more information, contact the Colloquium/Seminar
Coordinator, Dr. Hokwon Cho (To see Math Dept Colloquia/Seminars, click next: Math Dept Seminar) 
Fall 2012 
University of Nevada, Reno to Hydrology, Climate and Finance [Abstract] We consider the problem of modeling the
joint distribution of the duration, maximum and magnitude of stochastic
episodes (events). An event is defined as consecutive observations of a
process above (or below) a threshold. Examples of events include growth (or
decline) periods of a financial series or climatic or hydrologic episodes,
e.g. flood, draught, heat wave, cold spell, etc. Let N, X, and Y describe
episodes as their duration (N), magnitude (X) and maximum/peak (Y). The
distribution of the vector (N, X, Y) is of direct interest to water
management, energy management companies, disaster management, health
departments as well as state and federal
regulatory agencies. In particular, the probability of
extreme events associated with large values of (N, X, Y) are of
primary interest. We present the exact joint distribution of the vector (N,
X, Y) and its marginals and conditionals, when N
has a geometric distribution, X is the random sum and Y is the random maximum
of N iid exponential random variables. We also
provide error estimates for the computation of probabilities using models
from this family. We illustrate the modeling potential of these distributions
using hydroclimatic data.
University of Nevada, Las Vegas & EnvironStat Naturally Occurring and
Anthropogenic Beryllium in the Workplace [Abstract] Metal ratios can effectively predict the
amount of (nontoxic) natural beryllium (NBe) that should be found in an
insidefacility sample based on the amounts present of other metals
characteristic of minerals in background soils. The difference between the
total Be measurement and the predicted NBe for that sample is an estimate of
the amount of (toxic) anthropogenic Be (ABe) present. These ABe estimates
can then be used in determining whether the facility is “clean” using an
appropriate decision rule.
Friday, November 16 University of Maryland, Baltimore County [Abstract] During
the last fifteen years or so, generalized Pvalues
have become quite useful in solving testing problems in many nonstandard
situations. In this talk the notion of a generalized Pvalue will be
explained and its many applications will be presented. The application area
will mostly include linear models.
University of Trento, Italy [Abstract] The concept of regularly varying function is
an essential analytical tool for analyzing data in economics and finance,
since a remarkable number of regularities, or “laws”, are considered to
follow an approximate power law, at least in the upper tail, in these fields.
Available estimators of the tail index of a distribution are usually based on
extreme order statistics which are often too few to provide exhaustive
information on the problem. In the talk, a different approach, based on
a regressiontype estimator, which utilizes all sample data
will be considered. The method discussed exploits the fact that, under some
conditions, the behavior of the distribution function near infinity is
reflected in the behavior of the characteristic function near the origin. The
approach is semiparametric as only an assumption about the tail of the
distribution is used. Theoretical properties of the estimator, including its
bias, variance and asymptotic distribution are derived as well as rules for
its practical application.
Washington State University [Abstract] We consider an mdimensional vector autoregressive process Z_{t} of integrated
order 1, such that Z_{t}_{ } = (Y’_{t}_{ },X_{t}’)’ where Y_{t} is an m_{y}dimensional vector
process of endogenous variables and X_{t}_{ } is an m_{x}dimensional
vector process of exogenous variables with
m_{y} + m_{x} = m. We assume that there are r cointegrating
relations in Z_{t}. Pesaran et al.
(2000), Johansen (1992) and Harbo et al. (1998) considered inference of Z_{t}
assuming the weak exogeneity of X_{t}_{ }, that is, the nonstationary exogenous
variables are not cointegrated. We
consider the case where exogenous variables are cointegrated
with rank r_{x}
< m_{x}.
Parameterization and estimation of the model is considered, and the
asymptotic properties of the least square estimator and the maximum
likelihood estimator are presented. A
real data example is provided to illustrate the methods. Finite sample properties of the estimators
are also examined through a Monte Carlo simulation. 
Spring
2013 
Feb. 5 – Mar. 5 (Statistics
Position) Candidates’ Seminar
Department of Mathematics and Statistics University of Nevada, Reno Title: Laplace Probability
Distributions and Generalizations: An Excursion Beyond Normality [Abstract] Skew Laplace distributions, which naturally
arise in connection with random summation and quantile
regression settings, offer an attractive and flexible alternative to the
normal (Gaussian) distribution in a variety of settings where the assumptions
of symmetry and short tail are too restrictive. The growing popularity of the
Laplacebased models in recent years is due to their fundamental properties,
which include a sharp peak at the mode, heavier than Gaussian tails,
existence of all moments, infinite divisibility, and, most importantly,
random stability and approximation of geometric sums. Since the latter arise
quite naturally, these distributions provide useful models in diverse areas,
such as biology, economics, engineering, finance, geosciences, and physics.
We review fundamental properties of these models, which give insight into
their applicability in these areas, and discuss some extensions, including time
series and stochastic processes.
HoffmannLa Roche Inc., NJ from Health Care
Application [Abstract] The detection
of the change point in sequential data or the detection of the
starting/ending point of dropdown in longitudinal data is an important
problem in health care application. However, the recent data from diseases
spreading or clinical area are so complex that the existing methods may not
work well. In this presentation, we discuss several challenging issues in the
changepoint problems derived from complex data and propose better
approaches. In
the sequential change point problem from health care surveillance, the
baseline might exhibit a systematic pattern from population changes.
Furthermore, spatiotemporal surveillance should consider spatial features,
and regional data might be correlated. We propose the general framework based
on likelihood ratios to deal with such challenging issues. In the change
detection problem from the longitudinal data, the data in clinical areas such
as blood flow shows a flexible nonlinear curve with change points. We consider one approach based on the partial spline model,
and propose a modification to generalized
cross validation criteria for choosing the smoothing parameter in order to
reduce the bias of the change time estimate. Friday, May 3 Columbia University [Abstract] Sequential tests have been used commonly in
clinical trials to compare treatments. For sequential analysis of right
censored survival data with covariate adjustment, several different methods
have been studied in the literature. Here I review available methods and
present recent development in this topic. Numerical studies will also
be presented. 
