Overview

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.

Full Product Details

Author:   Luisa T. Fernholz ,  Stephan Morgenthaler ,  Werner Stahel
Publisher:   Birkhauser Verlag AG
Imprint:   Birkhauser Verlag AG
Dimensions:   Width: 15.50cm , Height: 1.20cm , Length: 23.50cm
Weight:   1.010kg
ISBN:  

9783764365752

ISBN 10:   3764365757
Pages:   183
Publication Date:   01 June 2001
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

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.

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