"Publication bias" is a relatively new statistical phenomenon that only arises when one attempts through a meta-analysis to review all studies, significant or insignificant, in order to provide a total perspective on a particular issue. This has recently received some notoriety as an issue in the evaluation of the relative risk of lung cancer associated with passive smoking, following legal challenges to a 1992 Environmental Protection Agency analysis which concluded that such exposure is associated with significant excess risk of lung cancer.

We introduce a Bayesian approach which estimates and adjusts for publication bias. Estimation is based on a data-augmentation principle within a hierarchical model, and the number and outcomes of unobserved studies are simulated using Gibbs sampling methods. This technique yields a quantitative adjustment for the passive smoking meta-analysis. We estimate that there may be both negative and positive but insignificant studies omitted, and that failing to allow for these would mean that the estimated excess risk may be overstated by around 30%, both in U.S. studies and in the global collection of studies.

In response to the growing problem of grade inflation in American undergraduate institutions, alternatives to GPA and GPA-based student assessment are discussed. One alternative summary, based on a Bayesian latent trait formulation, eliminates many of the inequities associated with GPA-based measures and has been proposed as a replacement for GPA-based class ranks at Duke University.

Since the time of Gauss, it has been generally accepted that $\ell_2$-methods of combining observations by minimizing sums of squared errors have significant computational advantages over earlier $\ell_1$-methods based on minimization of absolute errors advocated by Boscovich, Laplace and others. However, $\ell_1$-methods are known to have significant robustness advantages over $\ell_2$-methods in many applications, and related quantile regression methods provide a useful, complementary approach to classical least-squares estimation of statistical models. Combining recent advances in interior point methods for solving linear programs with a new statistical preprocessing approach for $\ell_1$-type problems, we obtain a 10- to 100-fold improvement in computational speeds over current (simplex-based) $\ell_1$-algorithms in large problems, demonstrating that $\ell_1$-methods can be made competitive with $\ell_2$-methods in terms of computational speed throughout the entire range of problem sizes. Formal complexity results suggest that $\ell_1$-regression can be made faster than least-squares regression for n sufficiently large and p modest.

Joseph L. Doob was born in Cincinnati, Ohio, February 27, 1910. He received the degrees A.B. in 1930, A.M. in 1931 and Ph.D. in 1932 from Harvard University. From 1932 to 1934 Doob did postdoctoral work at Columbia University. In 1933-1934 he held a Carnegie Corporation Fellowship. Doob was a member of the faculty of the University of Illinois from 1935 until his retirement in 1978. He was Commissar of the Champaign-Urbana Saturday Hike for about 25 years after World War II.

Doob is a member of the National Academy of Sciences and Foreign Associate of the Academy of Sciences, France. He was President of the Institute of Mathematical Statistics in 1950 and of the American Mathematical Society in 1963 and 1964. He received the National Medal of Science in 1979. In 1984 Doob was awarded the Career Prize by the American Mathematical Society for "his fundamental work in establishing probability as a branch of mathematics and for his continuing profound influence on its development."