Bayesian estimation and experimental design in linear regression models - Info and Reading Options
By Jürgen Pilz ( 1951 - )
“Bayesian estimation and experimental design in linear regression models” Metadata:
- Title: ➤ Bayesian estimation and experimental design in linear regression models
- Author: Jürgen Pilz
“Bayesian estimation and experimental design in linear regression models” Subjects and Themes:
- 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
Edition Identifiers:
- The Open Library ID: OL4979119W
AI-generated Review of “Bayesian estimation and experimental design in linear regression models”:
"Bayesian estimation and experimental design in linear regression models" Description:
The Open Library:
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.
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