Robert E. (Rob) Kass

Everything I've really needed to know (about Statistics), I learned in graduate school. A few basic ideas that appealed to me were: first, scientific inference, the process by which conclusions are drawn from data, may be formalized by applying Bayes' Theorem; second, asymptotic approximations (that is, approximations that assume the sample size is nearly infinite) are often illuminating; and third, geometry can help us better understand our mathematical manipulations. In addition, several consulting projects solidified my view that these notions have important consequences for statistical practice.

I am frequently reminded, especially when I play basketball with our graduate students, that it has been quite a while since I was in school. During the intervening years I've listened to, read, and thought about many problems in statistical theory and applications, but my main beliefs and interests have not drifted far from the basics I embraced back then. The papers I've written, at least the ones I take some pride in, have grown from the simple excitement of understanding something better, recognizing the implications and wanting to share the experience. Getting the picture right is rarely easy, but to glimpse the depth of fundamental concepts and witness the beauty that flows from them , it's worth the effort.

Most of the scientific projects I've offered advice about or collaborated on have come from medicine, psychology, and psychiatry. These experiences continue to shape my judgement about what is important in our discipline.

Some Related Publications

Kass, R.E. and Raftery, A.E. (1995) Bayes factors, Journal of the American Statistical Association, 90, pp. 773-795.

Kass, R.E. and Wasserman, L. (1996) The selection of prior distributions by formal rules, Journal of the American Statistical Association, 91, pp. 1343-1370.

Kass, R.E. (1989). "The geometry of asymptotic inference" (with discussion), Statistical Science, 4, pp. 188-234.