Robust Planning and Analysis of Experiments

1. Front Matter

   Pages i-x

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2. Efficient Inference for Planned Experiments

   1. Front Matter

      Pages 1-1

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   2. Planned Experiments

      • Christine H. Müller

      Pages 2-9

   3. Efficiency Concepts for Outlier-Free Observations

      • Christine H. Müller

      Pages 10-24

3. Robust Inference for Planned Experiments

   1. Front Matter

      Pages 25-25

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   2. Smoothness Concepts of Outlier Robustness

      • Christine H. Müller

      Pages 26-47

   3. Robustness Measures: Bias and Breakdown Points

      • Christine H. Müller

      Pages 48-74

   4. Asymptotic Robustness for Shrinking Contamination

      • Christine H. Müller

      Pages 75-97

   5. Robustness of Tests

      • Christine H. Müller

      Pages 98-107

4. High Robustness and High Efficiency

   1. Front Matter

      Pages 109-109

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   2. High Robustness and High Efficiency of Estimation

      • Christine H. Müller

      Pages 110-142

   3. High Robustness and High Efficiency of Tests

      • Christine H. Müller

      Pages 143-170

   4. High Breakdown Point and High Efficiency

      • Christine H. Müller

      Pages 171-183

   5. Outlook

      • Christine H. Müller

      Pages 184-188

5. Back Matter

   Pages 189-236

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Author-approved bcc: Robust statistics and the design of experiments are two of the fastest growing fields in contemporary statistics. Up to now, there has been very little overlap between these fields. In robust statistics, robust alternatives to the nonrobust least squares estimator have been developed, while in experimental design, designs for the efficient use of the least square estimator have been developed. This volume is the first to link these two areas by studying the influence of the design on the efficiency and robustness of robust estimators and tests. It shows that robust statistical procedures profit by an appropriate choice of the design and that efficient designs for a robust statistical analysis are more applicable. The classical approaches of experimental design and robust statistics are introduced before the areas are linked. Dr. Christine H. M ller teaches at the Department of Mathematics and Computer Science of the Free University of Berlin and is a member of the research project on "Efficient Experiments in Industrial Production." From 1988-1991, she worked as a biometrician at the Medical Department of the Free University of Berlin.

• Outlook
• Smooth function
• Volume
• computer science
• design
• function
• functional
• functions
• mathematics
• matrices
• modeling
• production
• robust statistics
• statistics
• university

• Fachbereich Mathematik und Informatik, WE1, Freie Universität Berlin, Berlin, Germany

  Christine H. Müller

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