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
