Limit this search to....

Constrained Statistical Inference
Contributor(s): Silvapulle (Author), Sen (Author)
ISBN: 0471208272     ISBN-13: 9780471208273
Publisher: John Wiley & Sons
OUR PRICE:   $180.45  
Product Type: Hardcover - Other Formats
Published: October 2004
Qty:
Annotation: 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
Additional Information
BISAC Categories:
- Mathematics | Probability & Statistics - Multivariate Analysis
Dewey: 519.535
LCCN: 2004048075
Series: Wiley Probability and Statistics
Physical Information: 1.2" H x 6.44" W x 9.56" (1.98 lbs) 532 pages
 
Descriptions, Reviews, Etc.
Publisher Description:
This volumes focuses on the theory of statistical inference under inequality constraints, providing a unified and up-to-date treatment of the methodology. The scope of applications of the presented methodology and theory in different fields is clearly illustrated by using examples from several areas, especially sociology, econometrics, and biostatistics. The authors also discuss a broad range of other inequality constrained inference problems, which do not fit well in the contemplated unified framework, providing meaningful access to comprehend methodological resolutions.