Department of Statistics Seminar
North Carolina State University

presents

Katja Remlinger, Kristi O'Grady, Ross Gosky and Yuliya Lokhnygina

North Carolina State University

A Sampler of Graduate students

• Katja Remlinger ksremlin@stat.ncsu.edu

  Statistical Design of Pools Using Optimal Coverage and Minimal Collision*
  Abstract: Discovery of a new drug involves screening large chemical libraries to identify active compounds. Screening efficiency can be improved by testing compounds in pools. We consider two criteria to design pools: optimal coverage of the chemical space and minimal collision between compounds. Five pooling designs are applied to a public data set. We evaluate each method by determining how well the design criteria are met. One design uniformly dominates all other designs, but all criteria-designed pools outperform randomly created pools. Furthermore, we discuss blocking and synergism between compounds as other effects that must be investigated when performing pooling experiments.
  *This a joint work with J. M. Hughes-Oliver (NCSU), S. S. Young (NISS) and R. L. H. Lam (GSK)

• Kristi O'Grady kgogrady@unity.ncsu.edu

  Combining Bayesian Networks and System Reliability to Meet Missile Defense Agency Needs
  Abstract: System reliability has often been viewed as a post-production statistical analysis. There exist systems, however, on such a large and costly scale that warrant the performing of a reliability analysis before initial system tests can be performed. In an effort to address this need the research in this talk focuses on the development and implementation of a Bayesian Network for "pre-test" reliability assessment. A motivating example from the Missile Defense Agency will be presented. Specific technical details include Bayesian network basics, and the Metropolis-Hasting's algorithm.

• Ross Gosky rmgosky@stat.ncsu.edu

  Model Selection for Bayesian Closed Population Capture Recapture Models*
  Abstract: We present Bayesian versions of 8 closed population Capture Recapture Models. These models incorporate variation in animal capture probabilities due to time effects, heterogeneity among animals, and behavioral effects after the first capture. For a given dataset, choosing which of the 8 models best fits the data is very important. Through simulations, we show that both Akaike's Information Criterion (AIC), and the Deviance Information Criterion (DIC) are useful tools in model selection. In particular, we assess whether these criteria are useful in selecting the correct model when average capture probabilities are between 20-30%.
  *This is a joint work with S. K. Ghosh (NCSU)

• Yuliya Lokhnyginaylokhny@unity.ncsu.edu

  Two-stage Optimal Group Sequential Designs*
  Abstract: Clinical trials are usually designed to have a particular power to detect some "clinically meaningful" treatment effect. It often happens, however, that the estimated treatment effect at the interim stage is smaller than this "clinically meaningful" alternative, but may still be important. Then the study might not have enough power to detect this smaller treatment difference. We propose a two-stage design which achieves the minimum expected sample size at the particular alternative and also has the desired power to detect a pre-specified smaller treatment difference. Our method is a modification of the "backward induction" algorithm applied to a Bayesian decision problem.
  *This is a joint work with A. A. Tsiatis (NCSU)

Friday, September, 19, 2003

3:35 - 4:35 pm

206 Cox Hall

Refreshments will be served on the second floor of Dabney Hall (left of Room 222) at 3:00 pm.