1994 Hartley Lectures

Department of Statistics
Texas A&M University

The Texas A&M University Department of Statistics

presents the

H.O. HARTLEY MEMORIAL LECTURES

October 10, 11 and 13, 1994

Lecturer: DAVID R. COX
Honorary Fellow of Nuffield College, Oxford, England

Causality, Graphical Methods, and Multivariate Analysis of Observed Data

The H. O. Hartley Memorial Lectures were established by the Texas A&M University Department of Statistics in 1988 to honor the memory of Herman Otto Hartley. Hartley accepted an appointment as Distinguished Professor at Texas A&M in 1963, founded Texas A&M's Institute of Statistics, and served as its director until his retirement in 1977. Hartley built his initial faculty of four into a group of 16, directed more than 30 doctoral students, and published over 75 papers during this period. He served as president of the American Statistical Association in 1979. Professor Hartley died on December 30, 1980.

Hartley was well known for his work on the foundations of sampling theory, and also made important contributions to mathematical optimization, estimation with incomplete data, estimation of variance components, and establishment of safe doses in carcinogenic experiments. Hartley collaborated with Egon Pearson to produce the classic two-volume Biometrika Tables for Statisticians.

Hartley earned a Ph.D. degree in mathematics at Berlin University in 1934, and a Ph.D. degree in statistics under John Wishart at Cambridge University in 1940. He taught at University College, London and Iowa State College before coming to Texas A&M. Hartley was deeply committed to all phases of his profession, including education, research, and delivery of knowledge and advice to users of statistics. H. O. Hartley was not only brilliant academician, but also a warm and caring human being. He is remembered with great fondness and respect at the 1994 lectures bearing his name.

The H.O. Hartley Memorial Lectures are held every other year, and were first given in 1988 by Peter J. Diggle, who is renowned for his work in the theory and application of spatial point processes. The lectures were presented in 1990 by Bradley Efron, a pioneer of bootstrapping methodology, and in 1992 by E.J. Hannan, a major figure in the theory and applications of time series analysis. The Department of Statistics is honored to have another distinguished scientist, Sir David R. Cox, present the 1994 Hartley Lectures.

Sir David R. Cox is an Honorary Fellow and former Warden of Nuffield College in Oxford, England. His remarkable career in statistical science is in its fifth decade, and is marked by work of fundamental importance. This work includes the introduction of widely applied methods such as the Box-Cox transformation and Cox's proportional hazards model.

Professor Cox was the editor of the prestigious and historic British journal Biometrika from 1966 to 1991. Among his many academic honors are Fellow of the Royal Statistical Society, Honorary Member of the American Academy of Arts and Sciences, Foreign Associate of the U.S. National Academy of Sciences, Honorary Fellow, Institute of Actuaries, and honorary doctorates from ten universities. Professor Cox has held a number of important offices in scientific organizations, including President of the Bernoulli Society, President of the Royal Statistical Society, and, currently, President-elect of the International Statistical Institute. He has also received many prestigious awards, including the Royal Statistical Society's Guy Medal in Gold, Oxford University's Weldon Memorial Prize, and the Kettering Prize and Gold Medal for Cancer Research. Sir David was knighted in 1985.

Each of Professor Cox's lectures will begin at 4:00 p.m. and will be held in Room 206 of the Texas A&M Memorial Student Center, College Station, TX. An outline of the lectures follows.

Causality, Graphical Models, and Multivariate Analysis of Observational Data

October 10 -- Causality: Some notion of cause is central to the scientist's efforts to explain the world. After some general discussion, four rather different notions of causality arising in a statistical context will be reviewed. Some implications for applied statistical work will be outlined.
October 11 -- Graphical Models: The notion of a graphical representation of complex relations goes back to Sewell Wright's method of path analysis in genetics. Some key ideas in graph theory will be introduced and used to classify models involving conditional independencies. Examples from observational studies will be given.
October 13 -- Graphical Models: Further Developments: Some further topics will be discussed including the use of derived variables, i.e., response variables that are combinations of initial measurements, the analysis of ordinal variables and the development of model checking procedures. The methodology discussed has applications in epidemiology, social science observational studies, and other areas.

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