A comparison of design and model selection methods for supersaturated experiments

Various design and model selection methods are available for supersatu-rated designs having more factors than runs but little research is available ontheir comparison and evaluation. In this paper, simulated experiments areused to evaluate the use of E(s2)-optimal and Bayesian D-optimal designs,and to compare three analysis strategies representing regression, shrinkageand a novel model-averaging procedure. Suggestions are made for choosingthe values of the tuning constants for each approach. Findings include that(i) the preferred analysis is via shrinkage; (ii) designs with similar numbersof runs and factors can be effective for a considerable number of active effectsof only moderate size; and (iii) unbalanced designs can perform well. Somecomments are made on the performance of the design and analysis methodswhen effect sparsity does not hol

Training samples in objective Bayesian model selection

Central to several objective approaches to Bayesian model selection is the use of training samples (subsets of the data), so as to allow utilization of improper objective priors. The most common prescription for choosing training samples is to choose them to be as small as possible, subject to yielding proper posteriors; these are called minimal training samples. When data can vary widely in terms of either information content or impact on the improper priors, use of minimal training samples can be inadequate. Important examples include certain cases of discrete data, the presence of censored observations, and certain situations involving linear models and explanatory variables. Such situations require more sophisticated methods of choosing training samples. A variety of such methods are developed in this paper, and successfully applied in challenging situations

A Comparison Study of Second-Order Screening Designs and Their Extension

Recent literature has proposed employing a single experimental design capable of preforming both factor screening and response surface estimation when conducting sequential experiments is unrealistic due to time, budget, or other constraints. Military systems, particularly aerodynamic systems, are complex. It is not unusual for these systems to exhibit nonlinear response behavior. Developmental testing may be tasked to characterize the nonlinear behavior of such systems while being restricted in how much testing can be accomplished. Second-order screening designs provide a means in a single design experiment to effectively focus test resources onto those factors driving system performance. Sponsored by the Office of the Secretary of Defense (ODS) in support of the Science of Test initiative, this research characterizes and adds to the area of second-order screening designs, particularly as applied to defense testing. Existing design methods are empirically tested and examined for robustness. The leading design method, a method that is very run efficient, is extended to overcome limitations when screening for non-linear effects. A case study and screening design guidance for defense testers is also provided

Advanced Statistical Tools for Six Sigma and other Industrial Applications

Six Sigma is a methodological approach and philosophy for quality improvement in operations management; its main objectives are identifying and removing the causes of defects, and minimizing variability in manufacturing and business processes. To do so, Six Sigma combines managerial and statistical tools, with the creation of a dedicated organizational structure. In this doctoral thesis and the three years of study and research, we have had the purpose to advance the potential applications of the methodology and its tools; with a specific attention on issues and challenges that typically prevent the realization of the expected financial and operational gains that a company pursue in applying the Six Sigma approach. Small and medium sized enterprises (SMEs), for instance, very often incur into such issues, for structural and infrastructural constraints. The overall application of the methodology in SMEs was the focus of the initial research effort and it has been studied with a case study approach. Then, on this basis, most of our research has been turned to the rigorous methodological advancement of specific statistical tools for Six Sigma, and in a broader sense, for other industrial applications. Specifically, the core contribution of this doctoral thesis lies in the development of both managerial and/or statistical tools for the Six Sigma toolbox. Our work ranges from a decision making tool, which integrates a response latency measure with a well-known procedure for alternatives prioritization; to experimental design tools covering both planning and analysis strategies for screening experiments; to, finally, an initial effort to explore and develop a research agenda based on issues related to conjoint analysis and discrete choice experiments.Six Sigma is a methodological approach and philosophy for quality improvement in operations management; its main objectives are identifying and removing the causes of defects, and minimizing variability in manufacturing and business processes. To do so, Six Sigma combines managerial and statistical tools, with the creation of a dedicated organizational structure. In this doctoral thesis and the three years of study and research, we have had the purpose to advance the potential applications of the methodology and its tools; with a specific attention on issues and challenges that typically prevent the realization of the expected financial and operational gains that a company pursue in applying the Six Sigma approach. Small and medium sized enterprises (SMEs), for instance, very often incur into such issues, for structural and infrastructural constraints. The overall application of the methodology in SMEs was the focus of the initial research effort and it has been studied with a case study approach. Then, on this basis, most of our research has been turned to the rigorous methodological advancement of specific statistical tools for Six Sigma, and in a broader sense, for other industrial applications. Specifically, the core contribution of this doctoral thesis lies in the development of both managerial and/or statistical tools for the Six Sigma toolbox. Our work ranges from a decision making tool, which integrates a response latency measure with a well-known procedure for alternatives prioritization; to experimental design tools covering both planning and analysis strategies for screening experiments; to, finally, an initial effort to explore and develop a research agenda based on issues related to conjoint analysis and discrete choice experiments