Friday, April 21, 2006
| Title |
Distributions, moments, inference problems |
| Speaker |
Jordan Stoyanov
Newcastle University, UK |
| Time |
3:00-4:00 p.m. |
| Place |
PHY 118 |
Abstract
The main discussion will be on distributions and their properties expressed
in terms of the moments. It will be clear how important is the distributions
we deal with to be uniquely determined by their moments.
Friday, March 31, 2006
| Title |
Statistical Analysis of Lightning Data |
| Speaker |
Rebecca Wooten |
| Time |
3:00-4:00 p.m. |
| Place |
PHY 118 |
Friday, March 24, 2006
| Title |
Analyzing lightning data using records |
| Speaker |
Alfred Mbah |
| Time |
3:00-4:00 p.m. |
| Place |
PHY 118 |
Abstract
We show by simulation that results obtained using record breaking data are
as good as the results obtained using the entire sample of size n.
We use records to analyze lightning data.
Friday, February 24, 2006
| Title |
Logistic Regression Approach to Software Reliability
Assessment: Early Estimation of Parameters |
| Speaker |
Louis Camara |
| Time |
3:00-4:00 p.m. |
| Place |
PHY 118 |
Abstract
While modeling software reliability data, is a topic of major importance that
has useful industrial applications, an early estimation of the number of fault
in the software would be very beneficial to the software developers. Assuming
the logistic model, an effective procedure for estimating the number of faults
in a software early in the testing and debugging phase will be presented. Using
real software failure data, we will illustrate the effectiveness of our results.
Friday, February 17, 2006
| Title |
Correlation of Storm Characteristics with Constituent Concentration in Urban Storm Water Discharges |
| Speaker |
L. Donald Duke, Ph.D., P.E.
Department of Environmental Science and Policy, USF |
| Time |
3:00-4:00 p.m. |
| Place |
PHY 118 |
Abstract
This research quantifies the correlation between characteristics of storm
events and the event mean concentration (EMC) of selected chemically-conservative
constituents in runoff originating from those events, using seven urban watersheds
in semi-arid coastal California cities. It features a method that employs
normality testing to identify extreme storm events, where EMCs have a different
mathematical relationship with storm characteristics than is the case with other
events. Removing extreme events provides a somewhat better correlation.
Friday, February 10, 2006
| Title |
On Simple Branching Processes that Grow Faster than Complete N-ary Trees |
| Speaker |
George Yanev |
| Time |
3:00-4:00 p.m. |
| Place |
PHY 118 |
Abstract
Branching processes are individual-based stochastic models for the growth of
populations. They have important applications in biology and epidemiology, among
others. What is the probability that a branching process grows faster than a
complete binary tree? What is the critical reproduction rate that makes this
probability positive? What is the distribution of the number of complete N-ary
subtrees of a branching tree? We will discuss the answers to these questions as
well as some open problems.
Friday, January 27, 2006
| Title |
On Characterizing Distributions with Conditional Expectations of Functions of Generalized Order Statistics |
| Speaker |
Dr. M. I. Beg
Visiting Professor
(joint work with M. Ahsanullah) |
| Time |
3:00-4:00 p.m. |
| Place |
PHY 118 |
Abstract
Let X(1,n,m,k),
X(2,n,m,k),
X(n,n,m,k) be n generalized
order statistics from an absolutely continuous distribution. We give
characterizations of distributions by means of
E{ψ(X(s,n,m,k))
| X(r,n,m,k) = x}
= g1(x) and E{ψ(X(r,
n,m,k)) |
X(s,n,m,k) = x}
= g2(x), s > r under
some mild conditions on ψ(.), gi(x), i =
1, 2. It is shown that most of the known characterization results based on
conditional expectations are special cases of the results of this paper.
Friday, January 20, 2006
| Title |
Analysis of Data from Response Guided Multiple-Baseline Designs |
| Speaker |
Dr. John Ferron
USF College of Education |
| Time |
3:00-4:00 p.m. |
| Place |
PHY 118 |
Abstract
Multiple-baseline designs are frequently used in educational contexts to make
treatment effect inferences. Multiple-baseline designs typically lead to the
collection of interrupted time series data on three to five participants. The
inferences are often drawn from short series lengths (less than 20 observations)
that arise from response guided experimentation (using the observed data to guide
decisions about how much data to collect before and after intervention). An
analysis of response guided multiple-baseline data will be presented.