Design of Experiments and Sample Surveys Commons^™

Statistical Designs For Learning User Preference And Experience, William S. Fisher

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For any organization, designing a new product or planning the features of a new service is a complex decision-making process which depends on understanding its users' preferences and the experiences they have with prototypes of the new product or service. Design of experiments (DOE) offers a framework to aid in the modeling and collection of data to learn user preferences and experiences to facilitate this decision-making process. Questions such as ``which of our newly proposed products is preferred most by our customers" and ``which version of our new service results in the highest monthly revenue" can be readily addressed by …

Statistical Methodologies For Sequential Online Controlled Experiments, Yangyi Li

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Online controlled experiments, primarily used on digital platforms like websites or apps, involve varying certain variables while keeping others constant to determine their effects on specific outcomes. This method allows researchers to compare results with a control group, gaining reliable insights. These experiments have grown popular among companies for assessing product impacts and guiding decision-making.

This dissertation presents a group Sequential Probability Ratio Testing (group SPRT) algorithm optimized for online experiments to balance sample number and accuracy by minimizing expected costs. It contrasts group SPRT with traditional SPRT under normal distributions, revealing differences in test power, sample size, and cost …

Machine Learning-Based Data And Model Driven Bayesian Uncertanity Quantification Of Inverse Problems For Suspended Non-Structural System, Zhiyuan Qin

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Inverse problems involve extracting the internal structure of a physical system from noisy measurement data. In many fields, the Bayesian inference is used to address the ill-conditioned nature of the inverse problem by incorporating prior information through an initial distribution. In the nonparametric Bayesian framework, surrogate models such as Gaussian Processes or Deep Neural Networks are used as flexible and effective probabilistic modeling tools to overcome the high-dimensional curse and reduce computational costs. In practical systems and computer models, uncertainties can be addressed through parameter calibration, sensitivity analysis, and uncertainty quantification, leading to improved reliability and robustness of decision and …

Development Of A Reverse Engineered, Parameterized, And Structurally Validated Computational Model To Identify Design Parameters That Influence American Football Faceguard Performance, William Ferriell

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Traumatic brain injury (TBI) continues to have the greatest incidence among athletes participating in American football. The headgear design research community has focused on developing accurate computational and experimental analysis techniques to better assess the ability of headgear technology to attenuate impacts and protect athletes from TBI. Despite efforts to innovate the headgear system, minimal progress has been made to innovate the faceguard. Although the faceguard is not the primary component of the headgear system that contributes to impact attenuation, faceguard performance metrics, such as weight, structural stiffness, and visual field occlusions, have been linked to athlete safety. To improve …