abstract: In this era of fast computational machines and new optimization algorithms, there have been great advances in Experimental Designs. We focus our research on design issues in generalized linear models (GLMs) and functional magnetic resonance imaging(fMRI). The first part of our research is on tackling the challenging problem of constructing exact designs for GLMs, that are robust against parameter, link and model uncertainties by improving an existing algorithm and providing a new one, based on using a continuous particle swarm optimization (PSO) and spectral clustering. The proposed algorithm is sufficiently versatile to accomodate most popular design selection criteria, and we concentrate on providing robust designs for GLMs, using the D and A optimality criterion. The second part of our research is on providing an algorithm that is a faster alternative to a recently proposed genetic algorithm (GA) to construct optimal designs for fMRI studies. Our algorithm is built upon a discrete version of the PSO. / Dissertation/Thesis / Doctoral Dissertation Statistics 2014

When approaching a new research problem, we often use screening designs to determine which factors are worth exploring in more detail. Before exploring a problem, we don't know which factors are important. When examining a large number of factors, it is likely that only a handful are significant and that even fewer two-factor interactions will be significant. If there are important interactions, it is likely that they are connected with the handful of significant main effects. Since we don't know beforehand which factors are significant, we want to choose a design that gives us the highest probability a priori of being able to estimate all significant main effects with their associated two-factor interactions. This project examines the methodology of finding designs that do not rely on an assumed model. We propose a method of modifying the D-Optimality criteria that averages over models with a common set of main effects and varying subsets of two-factor interations. We also calculate the proportion of the subsets that produce estimable designs. We use these results to find the best models for given run size and number of main effects.

Approved for public release; distribution is unlimited / To ensure sufficient capacity to handle unexpected demands for electric power, decision makers often over-estimate expeditionary power requirements. Therefore, we often use limited resources inefficiently by purchasing more generators and investing in more renewable energy sources than needed to run power systems on the battlefield. Improvement of the efficiency of expeditionary power units requires better managing of load requirements on the power grids and, where possible, shifting those loads to a more economical time of day. We analyze the performance of a previously developed optimization model for scheduling time-shiftable electrical loads in an expeditionary power grids model in two experiments. One experiment uses model data similar to the original baseline data, in which expected demand and expected renewable production remain constant throughout the day. The second experiment introduces unscheduled demand and realistic fluctuations in the power production and the demand distributions data that more closely reflect actual data. Our major findings show energy grid power production composition affects which uncertain factor(s) influence fuel con-sumption, and uncertainty in the energy grid system does not always increase fuel consumption by a large amount. We also discover that the generators running the most do not always have the best load factor on the grid, even when optimally scheduled. / Lieutenant Commander, United States Navy

在工業、農業、或醫藥界的實驗中，經常必須拿數個不同的試驗處理 (test treatments)和一個已使用過的對照處理(control treatment)比較 。所謂的試驗處理可能是數組新的儀器、不同配方的新藥、或不同成份的 肥料等。以實驗新藥為例，研藥者想決定是否能以新藥取代原來所使用的 藥，故對v種新藥與原藥做比較，評估其藥效之差異。為了降低實驗中不 必要的誤差以增加其準確性，集區設計成為實驗者常用的設計方法之一； 又因A式最適設計是我們欲估計的對照處理效果(effect)與試驗處理效果 之差異之估計值最小的設計，基於此良好的統計特性，我們選擇A式最適 性為評判根據。古典的A式最適性並未將對照處理與試驗處理所具備的先 前資訊(prior information)加以考慮，以上例而言，我們不可能對原來 使用的藥一無所知，經由過去的實驗或臨床的反應，研藥者必已對其藥性 有某種程度的了解，直觀上，這種過去經驗的累積，影響到實驗配置上， 可能使對照處理的實驗次數減少，相對地可對試驗處理多做實驗，設計遂 更具意義。因而本文考慮在k>v的情形下之貝式最適集區設計，對先前分 配施以某種限制，依據準確設計理論(exact design theory)，推導單項 異種消除模型(one- way elimination of heterogeneity model)之下的 貝氏A式最適設計與Γ- minimax最適設計，使Majumdar(1992)的結果能適 用於完全集區設計。此種設計對先前分配具有強韌性，即當先前分配有所 偏誤，且其誤差在某一範圍內時，此設計仍為最適設計或仍可維持所謂的 高效度(high efficiency)。本文將列舉許多實例以說明此一特性。 We consider the problem of comparing a set of v test treatments simultaneously with a control treatment when k>v. Following the work of Majumdar(1992), we use exact design theory to derive Bayes A-optimal designs and optimal Γ-minimax designs for the one-way elimination of heterogeneity model. These designs have the same properties as of Bayes A-optimal incomplete block designs. We also provide several examples of robust optimal designs and highly efficient designs.