Statistics in Genetics and in the Environmental Sciences / Edition 1

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Overview

This book grew out of a workshop on statistics in the sciences held on Monte Verit1, Switzerland, in the spring of 1999. It offers a snapsho t of the role played by statistics in genetics and in the environmenta l sciences. A few papers dwell on genetic topics, others deal with ris k assessment, in particular involving exposure to chemicals. Pollution is addressed in a survey of problems relating to atmospheric chemistr y, and in an article on space debris. The collection finally presents several contributions on modern statistical methods in the sciences. T he book will be particularly useful for statisticians who wish to be i nformed about the use of their methods in the sciences. They will also find a variety of open problems with explanations and solutions. On t he other hand, the book does not require a high degree of expertise in statistics and can, on the whole, be read profitably by researchers i n genetics and environmetrics.

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Product Details

  • ISBN-13: 9783764365752
  • Publisher: Birkhauser Basel
  • Publication date: 6/1/2001
  • Series: Trends in Mathematics Series
  • Edition description: 2001
  • Edition number: 1
  • Pages: 183
  • Product dimensions: 6.00 (w) x 9.30 (h) x 0.70 (d)

Table of Contents

Foreword
List of Authors and Coauthors
List of Participants
Statistical Interaction with Quantitative Geneticists to Enhance Impact from Plant Breeding Programs 1
Outlier Resistance, Standardization, and Modeling Issues for DNA Microarray Data 17
Variance Components Estimation with Uncertainty 27
Robust Estimation for Chemical Concentration Data Subject to Detection Limits 41
Risk Assessment of Low Dose Exposure to Carcinogens 65
A Stochastic Model of Carcinogenesis 77
Statistical Modelling to Answer Key Questions in Atmospheric Chemistry: Three Case Studies 89
Space Debris: Flux in a Two Dimensional Orbit 105
A Robust Approach to Common Principal Components 117
A Robsutified Version of Sliced Inverse Regression 147
Similarities Between Location Depth and Regression Depth 159
Approximate [tau]-Estimates for Linear Regression Based on Sub-sampling of Elemental Sets 173
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