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Synopsis
An up-to-date approach to understanding statistical inference
Statistical inference is finding useful applications in numerous fields, from sociology and econometrics to biostatistics. This volume enables professionals in these and related fields to master the concepts of statistical inference under inequality constraints and to apply the theory to problems in a variety of areas.
Constrained Statistical Inference: Order, Inequality, and Shape Constraints provides a unified and up-to-date treatment of the methodology. It clearly illustrates concepts with practical examples from a variety of fields, focusing on sociology, econometrics, and biostatistics.
The authors also discuss a broad range of other inequality-constrained inference problems that do not fit well in the contemplated unified framework, providing a meaningful way for readers to comprehend methodological resolutions.
Chapter coverage includes:
- Population means and isotonic regression
- Inequality-constrained tests on normal means
- Tests in general parametric models
- Likelihood and alternatives
- Analysis of categorical data
- Inference on monotone density function, unimodal density function, shape constraints, and DMRL functions
- Bayesian perspectives, including Stein’s Paradox, shrinkage estimation, and decision theory
"synopsis" may belong to another edition of this title.
About the Author
MERVYN J. SILVAPULLE, PhD, is an Associate Professor in the Department of Statistical Science at La Trobe University in Bundoora, Australia. He received his PhD in statistics from the Australian National University in 1981.
PRANAB K. SEN, PhD, is a Professor in the Departments of Biostatistics and Statistics and Operations Research at the University of North Carolina at Chapel Hill. He received his PhD in 1962 from Calcutta University, India.
Review
"This monograph provides an excellent coverage of the last twenty years of constrained statistical inference." (Journal of the American Statistical Association, March 2006)
"...an invaluable resource for any researcher with interests in constrained problems...it is easy to conclude that any statistical library would be incomplete without it." (Biometrics, December 2005)
"...a valuable source of information for statisticians working in any area..." (Mathematical Reviews, 2005k)
"About this title" may belong to another edition of this title.
- PublisherWiley-Interscience
- Publication date2004
- ISBN 10 0471208272
- ISBN 13 9780471208273
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages532
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Hardcover. Condition: new. Hardcover. An up-to-date approach to understanding statistical inference Statistical inference is finding useful applications in numerous fields, from sociology and econometrics to biostatistics. This volume enables professionals in these and related fields to master the concepts of statistical inference under inequality constraints and to apply the theory to problems in a variety of areas. Constrained Statistical Inference: Order, Inequality, and Shape Constraints provides a unified and up-to-date treatment of the methodology. It clearly illustrates concepts with practical examples from a variety of fields, focusing on sociology, econometrics, and biostatistics. The authors also discuss a broad range of other inequality-constrained inference problems that do not fit well in the contemplated unified framework, providing a meaningful way for readers to comprehend methodological resolutions. Chapter coverage includes: Population means and isotonic regressionInequality-constrained tests on normal meansTests in general parametric modelsLikelihood and alternativesAnalysis of categorical dataInference on monotone density function, unimodal density function, shape constraints, and DMRL functionsBayesian perspectives, including Steins Paradox, shrinkage estimation, and decision theory An up-to-date approach to understanding statistical inference Statistical inference is finding useful applications in numerous fields, from sociology and econometrics to biostatistics. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780471208273
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