Events Calendar

The Department of Statistics and Actuarial Science and its Annual Hogg and Craig Lecture Series present David A. Harville, professor emeritus in the Department of Statistics at Iowa State University.

David A. Harville served for 10 years as a mathematical statistician in the Applied Mathematics Research Laboratory of the Aerospace Research Laboratories (at Wright-Patterson AFB, Ohio), for 20 years as a full professor in Iowa State University’s Department of Statistics (where he now has emeritus status), and for 7 years as a research staff member of the Mathematical Sciences Department of IBM’s Thomas J. Watson Research Center. He is the author of more than 80 research articles and of 3 books: Matrix Algebra From a Statistician’s Perspective, Matrix Algebra: Exercises and Solutions, and Linear Models and the Relevant Distributions and Matrix Algebra. He has served as an associate editor of Biometrics and of the Journal of the American Statistical Association. He is a Fellow of both the American Statistical Association and the Institute of Mathematical Statistics and is an elected member of the International Statistical Institute.

Dr. Harville will present "Model-Based Prediction in General and in the Special Case of Ordinal Data".

Prediction problems are ubiquitous. In a model-based approach to predictive inference, the values of random variables that are presently observable are used to make inferences about the values of random variables that will become observable in the future, and the joint distribution of the random variables or various of its characteristics are assumed to be known up to the value of a vector of unknown parameters. Such an approach has proved to be highly effective in a wide variety of important applications. In cases where the model is taken to be a linear model and the form of the joint distribution to be multivariate normal, the implementation of a model-based approach is relatively tractable. And the results obtained for such cases can be extended to cases where the variables are ordinal in nature by relating the joint distribution of those variables to that of “latent variables.” The performance of a prediction procedure in “repeated application” may be important and can be evaluated from a theoretical (model-based) perspective and/or from empirical evidence. For purposes of illustration, the weekly prediction of the outcomes of college football games will be considered.

This is Lecture 2 of 2. Lecture 1 is on Thursday, April 25, at 3:30 p.m. in LR2 Van Allen Hall.

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