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Mathematics & Statistics

# Frontiers in Statistics (Leader: Prof. Chris Tsokos )

## Friday, April 21, 2006

Title
Speaker

Time
Place

Distributions, moments, inference problems
Jordan Stoyanov
Newcastle University, UK
3:00pm-4:00pm
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
Speaker
Time
Place

Statistical Analysis of Lightning Data
Rebecca Wooten
3:00pm-4:00pm
PHY 118

## Friday, March 24, 2006

Title
Speaker
Time
Place

Analyzing lightning data using records
Alfred Mbah
3:00pm-4:00pm
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
Speaker
Time
Place

Logistic Regression Approach to Software Reliability Assessment: Early Estimation of Parameters
Louis Camara
3:00pm-4:00pm
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
Speaker

Time
Place

Correlation of Storm Characteristics with Constituent Concentration in Urban Storm Water Discharges
L. Donald Duke, Ph.D., P.E.
Department of Environmental Science and Policy, USF
3:00pm-4:00pm
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
Speaker
Time
Place

On Simple Branching Processes that Grow Faster than Complete N-ary Trees
George Yanev
3:00pm-4:00pm
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
Speaker

Time
Place

On Characterizing Distributions with Conditional Expectations of Functions of Generalized Order Statistics
M. I. Beg
Visiting Professor
(joint work with M. Ahsanullah)
3:00pm-4:00pm
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 $$E\{\psi(X(s,n,m,k))\mid X(r,n,m,k)=x\}=g_1(x)$$ and $$E\{\psi(X(r,n,m,k))\mid X(s,n,m,k)=x\}=g_2(x)$$, $$s>r$$ under some mild conditions on $$\psi(.)$$, $$g_i(x)$$, $$i=1,2$$. 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
Speaker

Time
Place

Analysis of Data from Response Guided Multiple-Baseline Designs
John Ferron
USF College of Education
3:00pm-4:00pm
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.