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![]( "Buchcover ISBN 9781118165638")
„This monograph provides an excellent coverage of the last twentyyears of constrained statistical inference.“ (Journal of theAmerican Statistical Association, March 2006)
„... an invaluable resource for any researcher with interestsin constrained problems... it is easy to conclude that anystatistical library would be incomplete without it.“(Biometrics, December 2005)
„... a valuable source of information for statisticiansworking in any area...“ (Mathematical Reviews,2005k)
Constrained Statistical Inference
Order, Inequality, and Shape Constraints
von Mervyn J. Silvapulle und Pranab Kumar SenAn up-to-date approach to understanding statistical inference
Statistical inference is finding useful applications in numerousfields, from sociology and econometrics to biostatistics. Thisvolume enables professionals in these and related fields to masterthe concepts of statistical inference under inequality constraintsand to apply the theory to problems in a variety of areas.
Constrained Statistical Inference: Order, Inequality, and ShapeConstraints provides a unified and up-to-date treatment of themethodology. It clearly illustrates concepts with practicalexamples from a variety of fields, focusing on sociology, econometrics, and biostatistics.
The authors also discuss a broad range of otherinequality-constrained inference problems that do not fit well inthe contemplated unified framework, providing a meaningful way forreaders 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 densityfunction, shape constraints, and DMRL functions
* Bayesian perspectives, including Stein's Paradox, shrinkage estimation, and decision theory
Statistical inference is finding useful applications in numerousfields, from sociology and econometrics to biostatistics. Thisvolume enables professionals in these and related fields to masterthe concepts of statistical inference under inequality constraintsand to apply the theory to problems in a variety of areas.
Constrained Statistical Inference: Order, Inequality, and ShapeConstraints provides a unified and up-to-date treatment of themethodology. It clearly illustrates concepts with practicalexamples from a variety of fields, focusing on sociology, econometrics, and biostatistics.
The authors also discuss a broad range of otherinequality-constrained inference problems that do not fit well inthe contemplated unified framework, providing a meaningful way forreaders 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 densityfunction, shape constraints, and DMRL functions
* Bayesian perspectives, including Stein's Paradox, shrinkage estimation, and decision theory
