Statistics is strongly tied to applications in different scientific disciplines, and the most challenging statistical problems arise from problems in the sciences. In fact, the most innovative statistical research flows from the needs of applications in diverse settings. This volume is a testimony to the crucial role that statistics plays in scientific disciplines such as genetics and environmental sciences, among others. The articles in this volume range from human and agricultural genetic DNA research to carcinogens and chemical concentrations in the environment and to space debris and atmospheric chemistry. Also included are some articles on statistical methods which are sufficiently general and flexible to be applied to many practical situations. The papers were refereed by a panel of experts and the editors of the volume. The contributions are based on the talks presented at the Workshop on Statistics and the Sciences, held at the Centro Stefano Franscini in Ascona, Switzerland, during the week of May 23 to 28, 1999. The meeting was jointly organized by the Swiss Federal Institutes of Technology in Lausanne and Zurich, with the financial support of the Minerva Research Foundation. As the presentations at the workshop helped the participants recognize the po­ tential role that statistics can play in the sciences, we hope that this volume will help the reader to focus on the central role of statistics in the specific areas presented here and to extrapolate the results to further applications.

Inhalt

Statistical Interaction with Quantitative Geneticists to Enhance Impact from Plant Breeding Programs.- Outlier Resistance, Standardization, and Modeling Issues for DNA Microarray Data.- Variance Components Estimation with Uncertainty.- Robust Estimation for Chemical Concentration Data Subject to Detection Limits.- Risk Assessment of Low Dose Exposure to Carcinogens.- A Stochastic Model of Carcinogenesis.- Statistical Modelling to Answer Key Questions in Atmospheric Chemistry: Three Case Studies.- Space Debris: Flux in a Two Dimensional Orbit.- A Robust Approach to Common Principal Components.- A Robustified Version of Sliced Inverse Regression.- Similarities Between Location Depth and Regression Depth.- Approximate ?-Estimates for Linear Regression Based on Sub-sampling of Elemental Sets.