James L. Rosenberger, Ph.D.
Program Chair
Dr. Rosenberger has interests in statistical applications and the design of experiments for disciplines including agriculture, ecology, genomics, medicine, and transportation. Complex designs are frequently encountered in situations where physical restrictions, ethical limitations, or fiscal constraints prevent application of straightforward comparative experiment protocols. Nonstandard research designs often require nonstandard statistical analyses to reflect the degree of uncertainty and validity of an experiment.
Another area of interest has been statistical computing methodology and algorithms. He developed the algorithms for balanced, orthogonal analysis of variance, and more general linear model routines (now in the Minitab statistical package) that provide estimates and hypothesis tests for unbalanced data often encountered in observational studies.
Dr. Rosenberger served as department head from 1991 to 2006. Previously he served as the founding director of the Statistical Consulting Center. He is a Fellow of the American Statistical Association (ASA), and formerly was chair, program chair, and newsletter editor of the Statistical Computing Section of the ASA. During a leave from 1998 through 2000 he served as the statistics program director at the National Science Foundation. He was formerly an editor of the journal Statistical Science.
Steven F. Arnold, Ph.D.
Dr. Arnold is studying the effect of reducing by invariance before applying likelihood procedures. Examples indicate that in many situations the likelihood procedures are improved, and in nearly all situations they are no worse for this reduction. Certain types of invariance reductions may lead to generalization of the idea of ancillary statistics. Unfortunately, however, there are situations in which reducing by invariance may adversely affect likelihood procedures. Dr. Arnold hopes to find out when the invariance reduction can be taken safely and when it may lead to trouble.
His main research interest is statistical inference for models involving patterned covariance matrices. Although some attention is paid to methods for testing for the adequacy of these models, the primary emphasis is on finding procedures for drawing inference for the mean vector, when we assume that the covariance matrix has the assumed structure. Recently he has been studying antedependence models.
A second research interest is in improved estimators of the distribution function of samples of independently identically distributed random variables. One goal of this research is to find improved methods of doing bootstrapping, a procedure in which repeated samples are taken from the estimated distribution function and used to draw conclusions about the true distribution function, using very few assumptions.
Mosuk Chow, Ph.D.
Dr. Chow's areas of research interest include biostatistics, statistical decision theory, Bayesian inference, and sampling methods.
An important question in statistical decision theory is to characterize the set of all optimal procedures. An admissible procedure is optimal in the weak sense that it cannot be outperformed by another procedure completely in all circumstances. It is thus desirable to find necessary conditions for admissible procedures. Her work in decision theory involves finding such necessary conditions, investigating the admissibility properties of various estimators for problems arising from biology, genetics, and fishery.
Since for most cases a necessary condition for admissibility is that the procedure corresponds to a generalized Bayes rule, Dr. Chow's research also covers Bayesian inference. With recent advances in Bayesian computation methods, she has used Markov chain Monte Carlo methods in some of her work. Currently she is interested in Bayesian inference for aggregated distributions under various sampling schemes and Bayesian approach to problems related to biostatistics.
Laura J. Simon, Ph.D.
Dr. Simon is primarily interested in teaching statistical concepts to undergraduate math majors and to nonstatisticians in the biostatistical and health sciences fields. As her courses illustrate, she is a proponent of active, hands-on, and Web-based statistical education. Dr. Simon was nominated for the 2004 Eberly College of Science's C. I. Noll Award for Teaching Excellence. She was also nominated by two students and subsequently named to Who's Who Among America's Teachers in 2004 and 2005.
Dr. Simon co-authored nine units of the Web-based introductory statistics text, Visualizing Statistics (Cybergnostics, Inc.). She is a reviewer of statistical education resources submitted to CAUSEweb.org and merlot.org. Other professional interests include biostatistical consulting, repeated measures modeling, clinical trials development, research data management, and Web-page programming.
Before moving to the University Park campus in 1996, Dr. Simon worked at the Center for Biostatistics and Epidemiology at the Penn State College of Medicine at the Milton S. Hershey Medical Center. While there, she consulted with more than forty medical researchers and provided overall leadership to the data management unit on numerous projects, including the National Institutes of Health-funded National Interstitial Cystitis Database Study.
Prior to working at Penn State, Dr. Simon was a statistician at Auke Bay Research Laboratories in Auke Bay, Alaska, and a visiting summer statistician at Rohm and Haas Research Laboratories in Spring House, Pennsylvania.
Aleksandra B. Slavković, Ph.D.
Dr. Slavković's past and current research interests include usability evaluation methods, human performance in virtual environments, statistical data mining, application of statistics to social sciences, algebraic statistics, and statistical approaches to confidentiality and data disclosure. Her Ph.D. dissertation work focuses on statistical methodologies for disclosure limitation and data confidentiality, and presents new theoretical links between disclosure limitation, statistical theory, and computational algebraic geometry. It is a unique and interesting integration of diverse results from conditional specification of joint distribution, graphical models, disclosure limitation, and algebraic statistics.
Dr. Slavković served as a consultant to the National Academy of Sciences/National Research Council Committee to Review the Scientific Evidence on the Polygraph in 2001 and part of 2002. In 2003 she received an honorable mention for the best student paper from the Committee on Statisticians in Defense and National Security of the American Statistical Association.
Andrew J. Wiesner, Ph.D.
Dr. Wiesner's primary research interests involve educational statistics. He has presented several faculty workshops on interpreting item statistics to improve exams and the fundamentals of test item writing. Currently his research entails how frequent testing of students can improve their overall performance and increase learning. This includes the use of several Web tools that are available to deliver online assessments.
