Bayesian Estimation and Experimental Design in Linear Regression Models (Wiley Series in Probability and Statistics)

By Jürgen Pilz and Jürgen Pilz

Presents a clear treatment of the design and analysis of linear regression experiments in the presence of prior knowledge about the model parameters. Develops a unified approach to estimation and design; provides a Bayesian alternative to the least squares estimator; and indicates methods for the construction of optimal designs for the Bayes estimator. Material is also applicable to some well-known estimators using prior knowledge that is not available in the form of a prior distribution for the model parameters; such as mixed linear, minimax linear and ridge-type estimators.

• Format: Hardcover
• Language: English
• ISBN: 047191732X
• ISBN13: 9780471917328
• Release Date: July 1991
• Publisher: Wiley
• Length: 306 Pages
• Weight: 1.10 lbs.
• Dimensions: 9.3" x 0.9" x 6.3"