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In the design of experiments, optimal designs are a class of experimental designs that are optimal with respect to some statistical criterion. The creation of this field of statistics has been credited to Danish statistician Kirstine Smith. 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. Optimal designs are also called optimum designs.〔The adjective "optimum" (and not "optimal") "is the slightly older form in English and avoids the construction 'optim(um) + al´—there is no 'optimalis' in Latin" (page x in ''Optimum Experimental Designs, with SAS'', by Atkinson, Donev, and Tobias).〕 ==Advantages== Optimal designs offer three advantages over suboptimal experimental designs:〔These three advantages (of optimal designs) are documented in the textbook by Atkinson, Donev, and Tobias.〕 #Optimal designs reduce the costs of experimentation by allowing statistical models to be estimated with fewer experimental runs. #Optimal designs can accommodate multiple types of factors, such as process, mixture, and discrete factors. #Designs can be optimized when the design-space is constrained, for example, when the mathematical process-space contains factor-settings that are practically infeasible (e.g. due to safety concerns). 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Optimal design」の詳細全文を読む スポンサード リンク
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