Risk analysis may be defined as the problem of estimating the probabilities of rare events and the magnitudes of associated damages. The topic unifies the environmental sciences. This paper considers risk analyses for earthquakes, wildfires and floods. The computation of insurance premiums is used to motivate and unite the work.

This paper reviews the use of statistical methods in atmospheric science. The applications covered include the development, assessment and use of numerical physical models of the atmosphere and more empirical analysis unconnected to physical models.

Anthropogenic, or human-induced, climate change is a critical issue in science and in the affairs of humankind. Though the target of substantial research, the conclusions of climate change studies remain subject to numerous uncertainties. This article presents a very brief review of the basic arguments regarding anthropogenic climate change with particular emphasis on uncertainty.

Techniques for the analysis of spatial data have, to date, tended to ignore any effect caused by error in specifying the spatial locations at which measurements are recorded. This paper reviews the methods for adjusting spatial inference in the presence of data-location error, particularly for data that have a continuous spatial index (geostatistical data). New kriging equations are developed and evaluated based on a simulation experiment. They are also applied to remote-sensing data from the Total Ozone Mapping Spectrometer instrument on the Nimbus-7 satellite, where the location error is caused by assignment of the data to their nearest grid-cell centers. The remote-sensing data measure total column ozone (TCO), which is important for protecting the Earth's surface from ultraviolet and other radiation.

Both statistical ecology and environmental statistics have numerous challenges and opportunities in the waiting for the twenty-first century, calling for increasing numbers of nontraditional statistical approaches. Both theoretical and applied ecology are using advancing data analytical and interpretational software and hardware to satisfy public policy and discovery research, variously incorporating geospatial information, site-specific data and remote sensing imagery. We discuss a declared need for geoinformatic surveillance for spatial critical area detection. We explore, for ecological and environmental use, an innovation of the circle-based spatial scan statistic popular in the health sciences.

Environmental health research aims to discover and understand the links between environmental exposure and disease and to inform the regulatory community so that society can be protected against cancer, birth defects, and other adverse health effects associated with chemical, industrial and other exposures. Statistical science has a critical role to play in terms of providing the appropriate tools to design and analyze the studies needed to address the questions of interest, as well as quantifying risks and characterizing uncertainty. Recent years have seen some dramatic changes in the way that environmental risk assessment is accomplished. One such change is a move away from a traditional reliance on toxicological studies in animals to incorporate more epidemiological data. This shift has been facilitated by scientific advances that now allow researchers to accurately characterize human exposures in a variety of settings, as well as to measure genetic and other biomarkers that reflect subtle health effects and variations in susceptibility. This article will use a high profile case study to highlight some of the challenging statistical issues arising from this shifting emphasis from animal based toxicology to environmental epidemiology in the risk assessment world. Among the topics to be discussed are the uses of biologically based models and biomarkers, as well as the role of Bayesian methods to characterize uncertainty due to population heterogeneity, unmeasured confounders, exposure measurement error and model uncertainty.

Abdel El-Shaarawi was born on December 31, 1942, in Zagazig, Egypt. He received his B.Sc. and M.Sc. degrees in 1964 and 1968 from Cairo University and his Ph.D. in Statistics in 1973 from University of Waterloo. In 1973 he began a career as a research scientist at the Canada Centre for Inland Waters in Burlington, Ontario. He has been part-time Professor in the Department of Mathematics and Statistics, McMaster University, since 1980, and Adjunct Professor in the Department of Statistics and Actuarial Sciences, University of Western Ontario, 1986 to 1996, and in the Department of Statistics, University of British Columbia, since 2001. During 1983--1984 he was Visiting Professor at the University of Metz and during 2002--2003 at the University of Genoa. For shorter periods he has been Visiting Professor at the University of Kuwait (1998, 1999), Masaryk University (1998, 1999) and King Saud University (2000). He is founding Editor of the journal Environmetrics and founding President of The International Environmetrics Society. He is an elected member of the International Statistical Institute and a Fellow of the Royal Statistical Society (United Kingdom), the American Statistical Association and the Modelling and Simulation Society of Australia and New Zealand. Awards include the Distinguished Achievement Medal of the ASA Section on Statistics and the Environment and the Citation of Excellence Award from the Government of Canada.

This paper introduces graphical models as a natural environment in which to formulate and solve problems in genetics and related areas. Particular emphasis is given to the relationships among various local computation algorithms which have been developed within the hitherto mostly separate areas of graphical models and genetics. The potential of graphical models is explored and illustrated through a number of example applications where the genetic element is substantial or dominating.

Multipoint linkage analyses of data collected on related individuals are often performed as a first step in the discovery of disease genes. Through the dependence in inheritance of genes segregating at several linked loci, multipoint linkage analysis detects and localizes chromosomal regions (called trait loci) which contain disease genes. Our ability to correctly detect and position these trait loci is increased with the analysis of data observed on large pedigrees and multiple genetic markers. However, large pedigrees generally contain substantial missing data and exact calculation of the required multipoint likelihoods quickly becomes intractable. In this paper, we present a new Markov chain Monte Carlo approach to multipoint linkage analysis which greatly extends the range of models and data sets for which analysis is practical. Several advances in Markov chain Monte Carlo theory, namely joint updates of latent variables across loci or meioses, integrated proposals, Metropolis--Hastings restarts via sequential imputation and Rao--Blackwellized estimators, are incorporated into a sampling strategy which mixes well and produces accurate results in real time. The methodology is demonstrated through its application to several data sets originating from a study of early-onset Alzheimer's disease in families of Volga-German ethnic origin.