University of Pittsburgh

Faculty

Jay Gleser

Leon Jay Gleser
Professor

2732 Cathedral of Learning
412-624-3925

Education:

  • PhD (Statistics) from Stanford University (1963)
  • MS (Statistics) from Stanford University (1962)

  • BS (Mathematics) from the University of Chicago (1960)

Research Interests:

  • 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

Quote:

“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.”

Courses:

  • Spring 2007 (2074): STAT 1632, Intermediate Mathematical Statistics
  • Fall 2006 (2071): STAT 1631, Intermediate Probability
  • Fall 2006 (2071): STAT 2631, Theory of Statistics 1
  • Spring 2006 (2064): STAT 2270, Data Mining

Selected Publications:

  • 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.