Title: Bayesian estimation and experimental design in linear regression models
Description: 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.
Subjects: Bayesian statistical decision theory, Estimation theory, Experimental design, Regression analysis, Statistique bayesienne, Plan d'experience, Previsions economiques, Conception de systemes, Lineares Regressionsmodell, Lineares Modell, Analyse de regression, Analyse economique, Methodes de planification, Estimation, theorie de l', Modeles econometriques, Probabilites, Bayes-Verfahren, Methodes statistiques