Elimination of nuisance parameters is a central problem in statistical inference and has been formally studied in virtually all approaches to inference. Perhaps the least studied approach is elimination of nuisance parameters through integration, in the sense that this is viewed as an almost incidental byproduct of Bayesian analysis and is hence not something which is deemed to require separate study. There is, however, considerable value in considering integrated likelihood on its own, especially versions arising from default or noninformative priors. In this paper, we review such common integrated likelihoods and discuss their strengths and weaknesses relative to other methods.

Consideration of confounding is fundamental to the design and analysis of studies of causal effects. Yet, apart from confounding in experimental designs, the topic is given little or no discussion in most statistics texts. We here provide an overview of confounding and related concepts based on a counterfactual model for causation. Special attention is given to definitions of confounding, problems in control of confounding, the relation of confounding to exchangeability and collapsibility, and the importance of distinguishing confounding from noncollapsibility.

Musical performance theory and the theory of musical structure in general is a rapidly developing field of musicology that has wide practical implications. Due to the complex nature of music, statistics is likely to play an important role. In spite of this, up to the present, applications of statistical methods to music have been rare and mostly limited to a formal confirmation of results obtained by other methods. The present paper introduces a statistical approach to the analysis of metric, melodic and harmonic structures of a score and their influence on musical performance. Examples by Schumann, Webern and Bach illustrate the proposed method of numerical encoding and hierarchical decomposition of score information. Application to performance data is exemplified by the analysis of tempo data for Schumann's “Träumerei” op. 15/7. The paper demonstrates why statistics should play a major active part in performance research. The results obtained here are only a starting point and should, hopefully, stimulate a fruitful discussion between statisticians, musicologists, computer scientists and other researchers interested in the area.

The year 1997 marked the twenty­fifth anniversary of the foundation of the first Canadian statistical association and the silver jubilee of The Canadian Journal of Statistics (CJS). This paper relates the events and circumstances that led to the creation of these institutions. It also describes how frictions between individuals, as well as diverging regional and professional interests, soon led to the rise of a second, rival association that eventually merged with the first in 1977–78 to form what is now known as the Statistical Society of Canada (SSC). This historical account is based on abundant archival material and on interviews conducted by the authors in preparation for a commemorative presentation they made at the Annual Meeting of the SSC, June 2, 1997, in Fredericton, New Brunswick.

I. Richard Savage was born in Detroit on October 26, 1925. He attended Northern High School in Detroit and received a bachelor's degree in mathematics at the University of Chicago when he was nineteen. Subsequently, he received a master's degree in mathematics from the University of Michigan (1945) and a Ph.D. in mathematical statistics from Columbia in 1953. While at Columbia, he met JoAnn Osherow and they were married in 1950. They have two daughters, Martha, born in 1951, and Donna, born in 1953.

His career began with a three­year (1951–1954) stint as a mathematical statistician in the National Bureau of Standards, followed by a three­ year visiting appointment in the Department of Statistics at Stanford University. From 1957 to 1963, Savage was a professor of Statistics, Biostatistics and Economics at the University of Minnesota. He then spent the next eleven years on the faculty of Florida State University in the Department of Statistics. From 1974 until his retirement in 1990, he was a professor in the Department of Statistics at Yale University. His research interests include rank order statistics, statistics and pub­ lic policy and Bayesian statistics. He served as Coeditor of the Journal of the American Statistical Association (1968, as Editor of the Annals of Statistics (1974–1977), and as President of the American Statistical Association (1984). Because of his commitment to advance proper use of statistics to shape public policy, he has been heavily involved with the Committee on National Statistics of the National Research Council. The book Statistics and Public Policy, edited by Bruce Spencer, was published in 1997 in honor of Savage's contributions to statistics.