Optimal design | Design of Experiments, Optimization, Statistical Model, Bias of an Estimator, Minimum-Variance Unbiased Estimator, Replication, Statistical Theory, Bayesian Experimental Design

Lambert M. Surhone (u. a.)

ISBN 10: 6130337485 / ISBN 13: 9786130337483

Published by OmniScriptum, 2026

About this Item

Optimal design | Design of Experiments, Optimization, Statistical Model, Bias of an Estimator, Minimum-Variance Unbiased Estimator, Replication, Statistical Theory, Bayesian Experimental Design | Lambert M. Surhone (u. a.) | Taschenbuch | Englisch | 2026 | OmniScriptum | EAN 9786130337483 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 101384986

Bibliographic Details

• Title: Optimal design | Design of Experiments, Optimization, Statistical Model, Bias of an Estimator, Minimum-Variance Unbiased Estimator, Replication, Statistical Theory, Bayesian Experimental Design
• Publisher: OmniScriptum
• Publication Date: 2026
• Language: English
• ISBN 10: 6130337485
• ISBN 13: 9786130337483
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 5D millimeters 220 millimeters width by 150 millimeters height by 5 millimeters depth
• Weight: 125 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Optimal designs are a class of experimental designs that are optimal with respect to some statistical criterion. In the design of experiments for estimating statistical models, optimal designs allow parameters to be estimated without bias and with minimum-variance. A non-optimal design requires a greater number of experimental runs to estimate the parameters with the same precision as an optimal design. In practical terms, optimal experiments can reduce the costs of experimentation. The optimality of a design depends on the statistical model and is assessed with respect to a statistical criterion, which is related to the variance-matrix of the estimator. Specifying an appropriate model and specifying a suitable criterion function both require understanding of statistical theory and practical knowledge with designing experiments.