Ebook: Robust Planning and Analysis of Experiments

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.

---

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.

---

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.
Content:
Front Matter....Pages i-x
Front Matter....Pages 1-1
Planned Experiments....Pages 2-9
Efficiency Concepts for Outlier-Free Observations....Pages 10-24
Front Matter....Pages 25-25
Smoothness Concepts of Outlier Robustness....Pages 26-47
Robustness Measures: Bias and Breakdown Points....Pages 48-74
Asymptotic Robustness for Shrinking Contamination....Pages 75-97
Robustness of Tests....Pages 98-107
Front Matter....Pages 109-109
High Robustness and High Efficiency of Estimation....Pages 110-142
High Robustness and High Efficiency of Tests....Pages 143-170
High Breakdown Point and High Efficiency....Pages 171-183
Outlook....Pages 184-188
Back Matter....Pages 189-236

---

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.
Content:
Front Matter....Pages i-x
Front Matter....Pages 1-1
Planned Experiments....Pages 2-9
Efficiency Concepts for Outlier-Free Observations....Pages 10-24
Front Matter....Pages 25-25
Smoothness Concepts of Outlier Robustness....Pages 26-47
Robustness Measures: Bias and Breakdown Points....Pages 48-74
Asymptotic Robustness for Shrinking Contamination....Pages 75-97
Robustness of Tests....Pages 98-107
Front Matter....Pages 109-109
High Robustness and High Efficiency of Estimation....Pages 110-142
High Robustness and High Efficiency of Tests....Pages 143-170
High Breakdown Point and High Efficiency....Pages 171-183
Outlook....Pages 184-188
Back Matter....Pages 189-236
....