Education & Training
- PhD (Statistics) from Stanford University (1963)
- MS (Statistics) from Stanford University (1962)
- BS (Mathematics) from the University of Chicago (1960)
- Measurement and reporting of uncertainty
- Linear and nonlinear measurement error regression models
- Theories of statistical inference
- Statistical meta-analysis
- Data mining and statistical learning theory
- Applications of statistical models and methods to the biological, physical, and behavioral sciences
“Classical statistical papers and books, with some notable exceptions, have tended to treat statistical inference as being based on a single experiment, and concentrated on the design and inferential analysis of that one experiment. Science, however, is a process of learning and exploration, with each experiment motivating and guiding the next one. Modern statistics has started to give greater consideration to designing this process of learning: for example, data mining and statistical learning theory, statistical meta-analysis and adaptive clinical trials. In the learning process, communication from one investigator to another of well-understood and meaningful measures of uncertainty is essential.”
- Probability Models and Applications (with C. Derman and I. Olkin). Macmillan, New York, 1980. Second Edition, 1994.
- Estimation in a multivariate “errors in variables” regression model: Large sample results. Annals of Statistics, 9 (1981), 24-44.
- The effect of positive dependence on chi-squared tests for categorical data (with D. S. Moore). Journal of the Royal Statistics Society, Series B 47 (1985), 459-465.
- The nonexistence of 100(1-α)% confidence sets of finite expected diameter in errors-in-variables and related models (with J.T. Hwang). Annals of Statistics, 15 (1987), 1351-1362.
- Improvements of the naive approach to estimation in nonlinear errors-in-variables regression models. Contemporary Mathematics, 112 (1990), 99-114.
- Stochastically dependent effect sizes (with I. Olkin). Chapter 22 in Handbook of Research Synthesis (H. Cooper and L. Hedges, Eds.). Russell Sage Foundation, New York, 1994.
- Models for estimating the number of unpublished studies (with I. Olkin). Statistics in Medicine, 15 (1996), 2493-2507.
- The importance of assessing measurement reliability in multivariate regression. Journal of the American Statistics Association, 87 (1992), 696-707.
- Assessing uncertainty in measurement. Statistical Science, 13 (1998), 277-290.