Statistics in Genetics and in the Environmental Sciences

About this book

This book grew out of a workshop on statistics in the sciences held on Monte Verita, Switzerland, in the spring of 1999. It offers a snapshot of the role played by statistics in genetics and in the environmental sciences. A few papers dwell on genetic topics, others deal with risk assessment, in particular involving exposure to chemicals. Pollution is addressed in a survey of problems relating to atmospheric chemistry, and in an article on space debris. The collection finally presents several contributions on modern statistical methods in the sciences. The book should be particuarly useful for statisticians who wish to be informed about the use of their methods in the sciences. They will also find a variety of open problems with explanations and solutions. On the other hand, the book does not require a high degree of expertise in statistics and can, on the whole, be read profitably by researchers in genetics and environmetrics.

Contents

Statistical interaction with quantitative geneticists to enhance impact from plant breeding programmes, Kaye E. Basford; outlier resistance, standardization and modelling issues for DNA microarray data, Dhammika Amaratunga and Javier Cabrera; variance components estimation with uncertainty, Xiaoming Sheng and Chris Field; robust estimation for chemical concentration data subject to detection limits, Leo R. Korn and David E. Tyler; risk assessment of low dose exposure to carcinogens, Mendel Fygenson; a stochastic model of carcinogenesis, Pablo Herrero et al; statistical modelling to answer key questions in atmospheric chemistry - three case studies, Johannes Staehelin and Werner A. Stahel; space debris - flux in a two dimensional orbit, David R. Brillinger; a robust approach to common principal components, Graciela Boente and Liliana Orellana; a robustified version of sliced inverse regression, Ursula Gather et al; similarities between location depth and regression depth, Mia Hubert et al; approximate t-estimates for linear regression based on subsampling of elemental sets, Jorge Adrover et al.

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