Bayesian Estimation and Experimental Design in Linear Regression Models
Author: Jürgen Pilz
Publisher:
Published: 1991-07-09
Total Pages: 316
ISBN-13:
DOWNLOAD EBOOKPresents 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.