Pranab Kumar Sen

Mervyn J. Silvapulle

An 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

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