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- catalog abstract "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.".
- catalog contributor b10384271.
- catalog created "1997.".
- catalog date "1997".
- catalog date "1997.".
- catalog dateCopyrighted "1997.".
- catalog description "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.".
- catalog description "Efficient Inference for Planned Experiments: Planned Experiments. Efficiency Concepts for Outlier-Free Observations -- Robust Inference for Planned Experiments: Smoothness Concepts of Outlier Robustness. Robustness Measures: Bias and Breakdown Points -- Asymptotic Robustness for Shrinking Contamination -- Robustness of Tests -- High Robustness and High Efficiency: High Robustness and High Efficiency of Estimation. High Resolution and High Efficiency of Tests. High Breakdown Point and High Efficiency.".
- catalog description "Includes bibliographical references and index.".
- catalog extent "x, 234 p. ;".
- catalog hasFormat "Robust planning and analysis of experiments.".
- catalog identifier "038798223X (softcover : alk. paper)".
- catalog isFormatOf "Robust planning and analysis of experiments.".
- catalog isPartOf "Lecture notes in statistics (Springer-Verlag) ; v. 124.".
- catalog isPartOf "Lecture notes in statistics ; 124".
- catalog issued "1997".
- catalog issued "1997.".
- catalog language "eng".
- catalog publisher "New York : Springer,".
- catalog relation "Robust planning and analysis of experiments.".
- catalog subject "519.5/4 21".
- catalog subject "Experimental design.".
- catalog subject "QA279 .M85 1997".
- catalog subject "Robust statistics.".
- catalog subject "Statistics.".
- catalog tableOfContents "Efficient Inference for Planned Experiments: Planned Experiments. Efficiency Concepts for Outlier-Free Observations -- Robust Inference for Planned Experiments: Smoothness Concepts of Outlier Robustness. Robustness Measures: Bias and Breakdown Points -- Asymptotic Robustness for Shrinking Contamination -- Robustness of Tests -- High Robustness and High Efficiency: High Robustness and High Efficiency of Estimation. High Resolution and High Efficiency of Tests. High Breakdown Point and High Efficiency.".
- catalog title "Robust planning and analysis of experiments / Christine Müller.".
- catalog type "text".