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1
周敬良 e King-leung Chow. "Optimal designs for supplementary experiments". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1989. http://hub.hku.hk/bib/B31231548.
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Kao, Lie-Jane. "Designs for drug combination experiments /". The Ohio State University, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=osu14878493772931.
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Chow, King-leung. "Optimal designs for supplementary experiments /". [Hong Kong] : University of Hong Kong, 1993. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13787457.
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Leatherman, Erin Rae. "Optimal predictive designs for experiments that involve computer simulators". The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1376393067.
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Kim, Sungil. "Experimental design methods for nano-fabrication processes". Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42738.
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Most design of experiments assumes predetermined design regions. Design regions with uncertainty are of interest in the first chapter. This chapter proposes optimal designs under a two-part model to handle the uncertainty in the design region. In particular, the logit model in the two-part model is used to assess the uncertainty on the boundary of the design region. The second chapter proposes an efficient and effective multi-layer data collection scheme (Layers of Experiments) for building accurate statistical models to meet tight tolerance requirement commonly encountered in nano-fabrication. "Layers-of-Experiments" (LOE) obtain sub-regions of interest (layer) where the process optimum is expected to lie and collect more data in the sub-regions with concentrated focus. The third chapter contributes a new design criterion combining model-based optimal design and model-free space-filling design in a constraint manner. The proposed design is useful when the fitted statistical model is required to have both characteristics: accuracy in statistical inference and design space exploration. The fourth chapter proposes adaptive combined designs in the layers of experiments. This chapter also develops methods to improve model quality by combining information from various layers and from engineering models. Combined designs are modified to improve its efficiency by incorporate collected field data from several layers of experiments. Updated engineering models are used to build more accurate statistical models.
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Tsai, Hsing-Chuan. "Optimal designs for drug combination experiments /". The Ohio State University, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488193272068802.
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Persson, Johan. "Restricted Region Exact Designs". Thesis, Linköpings universitet, Matematiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138614.
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Problem statement: The D-optimal design is often used in clinical research. In multi-factor clinical experiments it is natural to restrict the experiment's design space so as not to give a patient the combination of several high dose treatments simultaneously. Under such design space restrictions it is unknown what designs are D-optimal. The goal of the thesis has been to find D-optimal designs for these design spaces. Approach: Two new algorithms for finding D-optimal designs with one, two or three factors with linear models has been developed and implemented in MATLAB. Two restricted design spaces were explored. In cases when the program could not find the D-optimal design an analytic approach was used. Results: Special attention was given to the two factor model with interaction. All of the D-optimal designs for this model, N less or equal to 30, and their permutations have been listed as well as their continous designs. Conclusion: In one of the restricted design regions a simple design pattern appeared for N greater than or equal to 7. In the other restricted design region no obvious pattern was found but its continuous design could be calculated through analysis. It turned out that the number of trials at the lowest dose combination did not change when moving from the full space design to the restricted design regions.Frågeställning: D-optimala designer är vanliga i kliniska studier. När flera faktorer (läkemedel) prövas samtidigt kan det vara nödvändigt att begränsa försöksrummet så att patienterna undviker att få en hög dos av flera faktorer samtidigt. I sådana begränsade försöksrum är det okänt vilka designer som är D-optimala. Uppsatsens mål har varit att hitta D-optimala designer i begränsade försöksrum. Metod: Två nya algoritmer för att hitta D-optimala designer med en, två eller tre dimensioner och linjära modeller har utvecklats och implementerats i MATLAB. Två begränsade försöksrum har utforskats. I de fall då MATLAB-programmet inte kunde hitta de D-optimala designerna användes analytiska metoder. Resultat: Analys av en tvåfaktormodell med interaktion utforskades särskilt noggrant. Alla D-optimala designer och permutationer av dessa i de båda begränsade försöksrummen har listats för alla N mindre än eller lika med 30, samt även deras kontinuerliga designer. Slutsats: För det ena försöksrummet upptäcktes ett mönster i designen då N är större än eller lika med 7. I det andra försöksrummet upptäcktes inget mönster och det krävdes således analytiska metoder för att finna dess kontinuerliga design. Det visade sig att antalet försök i den lägsta doskombinationen förblev oförändrat då man bytte från det fulla designrummet till de båda begränsade designrummen.
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Gustafsson, Emelie Anna. "Designs for computer experiments : random versus rational". Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/42946.
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Computer experiments facilitate, through mathematical modelling, various experiments that would otherwise be very difficult to perform, or even impossible. Computer experiments are employed to emulate situations in areas such as weather modelling, astrophysics, economics and many more.
In conducting a computer experiment, we are required to select a design for the initial values x⁽¹⁾, . . . , x(n) of a process, and then emulate the output based on the process and the initial values. The quality of the emulator therefore depends partly on the process and partly on the choice of initial values. We will only consider the Gaussian Process in this thesis, and the focus of our analysis will be on the selection of initial values. We consider the effects of selecting a random versus a rational design for the initial values of computer experiments.
We aim to study a broad range of possible computer models that are likely to arise in practice. Therefore, we present the analysis of eight different design choices, each of which is either random or rational, for the initial values. These initial values are applied to five different test functions that we consider to be prevalent in practice. We observe the effect of each of the designs on each of the test functions at three different sample sizes.
The goal of this thesis is to present a comprehensive study of various standard design choices and make recommendations to a practitioner on a robust design for the initial values of a computer experiment on a given function. As well as ultimately recommend a universally robust design for the initial values of any computer experiment.
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Fainaru, Ilana Carleton University Dissertation Mathematics and statistics. "F-optimal designs for binary response experiments". Ottawa, 1994.
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Volkov, Oleg. "Optimal relaxed designs of experiments, with pharmaceutical applications". Thesis, Queen Mary, University of London, 2014. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8297.
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This thesis was motivated by the collaborative research undertaken by QMUL and Pfizer UK into improving experiments at pre-clinical drug development. In theory, the most efficient designs for these particular experiments, as well as for many other studies, are optimal designs. Since, however, their implementation poses challenges — and several emerged during the project — optimal designs are uncommon in practice. To address these challenges the thesis introduces a comprehensive design framework, which both generalizes and simplifies optimal design. At the core of this framework are optimal relaxed designs, seldom considered before. Like a standard design measure a relaxed design has non-integer replications and is mathematically tractable; unlike the former, whose replications must sum to one, it allows the replications total to be unconstrained. The methodology discussed in this thesis assumes design of experiments for parameter estimation, given a response model, but also applies to broader problems. Although the motivation and applications come from the pharmaceutical project, the ultimate goal is to develop an intuitive and versatile design toolkit for experimenters in various practical fields.
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Sun, Fangfang. "On A-optimal Designs for Discrete Choice Experiments and Sensitivity Analysis for Computer Experiments". The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1345231162.
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Tsai, Pi-Wen. "Three-level designs robust to model uncertainty". Thesis, University of Reading, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389644.
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Vahl, Christopher Irvin. "Algortihms for constructing D-optimal designs for multilevel experiments /". Search for this dissertation online, 2005. http://wwwlib.umi.com/cr/ksu/main.
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Leesinsky, Peter. "Student designs of experiments as indicators of physics reasoning". Thesis, McGill University, 1991. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=70250.
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The purpose of this study was the assessment of physics reasoning on the basis of students' understanding of motion on an inclined plane. Subjects were presented with a video tape showing a motion experiment in steps and were asked to formulate hypotheses and design an experiment to test these. Subjects thought aloud while specifying the designs and goals of an experiment. Protocols were analyzed by an original method using schema representation techniques. Adequancy of subjects' reasoning was evaluated by comparison to a composite model built from physics domain principles. As more information was presented to subjects, processing differences were observed. Using a hierarchy of processes from recognition to generation, five groups of subjects were defined. Subjects differed in recognition and inclusion processes, use of incoming information, ability to generate experimental designs, and responses to falsification. Concepts of average velocity and differences in directionality of reasoning were analyzed.
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Marley, Christopher J. "Screening experiments using supersaturated designs with application to industry". Thesis, University of Southampton, 2011. https://eprints.soton.ac.uk/176451/.
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This thesis describes the statistical methodology behind a variety of industrial screening experiments. The primary focus of the thesis is on supersaturated designs that have more parameters to be investigated than runs available. Such designs are particularly useful when experiments are expensive to perform. In addition, the statistical issues behind a real-life screening experiment are investigated, where there is a functional response and the factor levels cannot be set directly. A study to compare several existing design and analysis methods for two-level supersaturated designs was carried out. A variety of different scenarios in terms of numbers of runs, numbers of factors and numbers of active factors were investigated via simulated experiments. The Gauss-Dantzig selector was identified as an effective analysis method, whilst little difference was found in the practical performance of designs from the different criteria. As a result of the study, several guidelines are provided, to indicate when supersaturated designs are most likely to be effective as a screening tool. A new criterion for designing supersaturated experiments under measures of multicollinearity is presented. The criterion is particularly applicable to experiments where factor levels cannot be set independently, although its application to two-level designs is also demonstrated. An optimal allocation of factors to columns of an existing design is also considered. Supersaturated experiments are discussed in the context of robust product design, where the interactions between control factors and noise factors are explored. A new criterion specifically applicable to supersaturated robust product design experiments is described. The fact that the experimenter is interested in some parameters more than others is exploited and the cost savings from using a supersaturated experiment are illustrated. It is demonstrated that substantial gains in power to detect active effects can be achieved when using this new criterion. Finally, the design and analysis of a practical screening experiment is discussed. Complicating features of the experiment include the multivariate nature of the response and the fact that factor levels cannot be set directly. A two-stage linear mixed effect model is applied, with principal components analysis used for the first stage models. A novel method for finding follow-up runs to the screening experiment is described and implemented
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Hilow, Hisham. "Economic expansible-contractible sequential factorial designs for exploratory experiments". Diss., Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/54426.
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Sequential experimentation, especially for factorial treatment structures, becomes important when one or more of the following, conditions exist: observations become available quickly, observations are costly to obtain, experimental results need to be evaluated quickly, adjustments in experimental set-up may be desirable, a quick screening of the importance of various factors is important. The designs discussed in this study are suitable for these situations. Two approaches to sequential factorial experimentation are considered: one-run-at-a-time (ORAT) plans and one-block-at-a-time (OBAT) plans.
For 2ⁿ experiments, saturated non-orthogonal 2ᵥⁿ fractions to be carried out as ORAT plans are reported. In such ORAT plans, only one factor level is changed between any two successive runs. Such plans are useful and economical for situations in which it is costly to change simultaneously more than one factor level at a given time. The estimable effects and the alias structure after each run have been provided. Formulas for the estimates of main-effects and two-factor interactions have been derived. Such formulas can be used for assessing the significance of their estimates.
For 3m and 2ⁿ3m experiments, Webb's (1965) saturated non-orthogonal expansible-contractible <0, 1, 2> - 2ᵥⁿ designs have been generalized and new saturated non-orthogonal expansible-contractible 3ᵥm and 2ⁿ3ᵥm designs have been reported. Based on these 2ᵥⁿ, 3ᵥm and 2ⁿ3ᵥm designs, we have reported new OBAT 2ᵥⁿ, 3ᵥm and 2ⁿ3ᵥm plans which will eventually lead to the estimation of all main-effects and all two-factor interactions. The OBAT 2ⁿ, 3m and 2ⁿ3m plans have been constructed according to two strategies: Strategy I OBAT plans are carried out in blocks of very small sizes, i.e. 2 and 3, and factor effects are estimated one at a time whereas Strategy II OBAT plans involve larger block sizes where factors are assumed to fall into disjoint sets and each block investigates the effects of the factors of a particular set. Strategy I OBAT plans are appropriate when severe time trends in the response may be present. Formulas for estimates of main-effects and two-factor interactions at the various stages of strategy I OBAT 2ⁿ, 3m and 2ⁿ3m plans are reported.Ph. D.
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Donley, David Wayne. "Optimal Designs for Computer Experiments for Determining Optimal Operating Conditions /". The Ohio State University, 1996. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487933648649737.
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Tan, Matthias H. Y. "Contributions to quality improvement methodologies and computer experiments". Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/48936.
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This dissertation presents novel methodologies for five problem areas in modern quality improvement and computer experiments, i.e., selective assembly, robust design with computer experiments, multivariate quality control, model selection for split plot experiments, and construction of minimax designs.
Selective assembly has traditionally been used to achieve tight specifications on the clearance of two mating parts. Chapter 1 proposes generalizations of the selective assembly method to assemblies with any number of components and any assembly response function, called generalized selective assembly (GSA). Two variants of GSA are considered: direct selective assembly (DSA) and fixed bin selective assembly (FBSA). In DSA and FBSA, the problem of matching a batch of N components of each type to give N assemblies that minimize quality cost is formulated as axial multi-index assignment and transportation problems respectively. Realistic examples are given to show that GSA can significantly improve the quality of assemblies.
Chapter 2 proposes methods for robust design optimization with time consuming computer simulations. Gaussian process models are widely employed for modeling responses as a function of control and noise factors in computer experiments. In these experiments, robust design optimization is often based on average quadratic loss computed as if the posterior mean were the true response function, which can give misleading results. We propose optimization criteria derived by taking expectation of the average quadratic loss with respect to the posterior predictive process, and methods based on the Lugannani-Rice saddlepoint approximation for constructing accurate credible intervals for the average loss. These quantities allow response surface uncertainty to be taken into account in the optimization process.
Chapter 3 proposes a Bayesian method for identifying mean shifts in multivariate normally distributed quality characteristics. Multivariate quality characteristics are often monitored using a few summary statistics. However, to determine the causes of an out-of-control signal, information about which means shifted and the directions of the shifts is often needed. We propose a Bayesian approach that gives this information. For each mean, an indicator variable that indicates whether the mean shifted upwards, shifted downwards, or remained unchanged is introduced. Default prior distributions are proposed. Mean shift identification is based on the modes of the posterior distributions of the indicators, which are determined via Gibbs sampling.
Chapter 4 proposes a Bayesian method for model selection in fractionated split plot experiments. We employ a Bayesian hierarchical model that takes into account the split plot error structure. Expressions for computing the posterior model probability and other important posterior quantities that require evaluation of at most two uni-dimensional integrals are derived. A novel algorithm called combined global and local search is proposed to find models with high posterior probabilities and to estimate posterior model probabilities. The proposed method is illustrated with the analysis of three real robust design experiments. Simulation studies demonstrate that the method has good performance.
The problem of choosing a design that is representative of a finite candidate set is an important problem in computer experiments. The minimax criterion measures the degree of representativeness because it is the maximum distance of a candidate point to the design. Chapter 5 proposes algorithms for finding minimax designs for finite design regions. We establish the relationship between minimax designs and the classical set covering location problem in operations research, which is a binary linear program. We prove that the set of minimax distances is the set of discontinuities of the function that maps the covering radius to the optimal objective function value, and optimal solutions at the discontinuities are minimax designs. These results are employed to design efficient procedures for finding globally optimal minimax and near-minimax designs.
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LAM, CHEN QUIN. "Sequential Adaptive Designs In Computer Experiments For Response Surface Model Fit". The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1211911211.
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Nyarko, Eric Verfasser], e Rainer [Gutachter] [Schwabe. "Optimal designs for paired comparison experiments / Eric Nyarko ; Gutachter: Rainer Schwabe". Magdeburg : Universitätsbibliothek Otto-von-Guericke-Universität, 2019. http://d-nb.info/1219965790/34.
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Nyarko, Eric [Verfasser], e Rainer [Gutachter] Schwabe. "Optimal designs for paired comparison experiments / Eric Nyarko ; Gutachter: Rainer Schwabe". Magdeburg : Universitätsbibliothek Otto-von-Guericke-Universität, 2019. http://nbn-resolving.de/urn:nbn:de:gbv:ma9:1-1981185920-321555.
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Hou, Ruizhe. "Optimal Latin Hypercube Designs for Computer Experiments Based on Multiple Objectives". Scholar Commons, 2018. http://scholarcommons.usf.edu/etd/7169.
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Latin hypercube designs (LHDs) have broad applications in constructing computer experiments and sampling for Monte-Carlo integration due to its nice property of having projections evenly distributed on the univariate distribution of each input variable. The LHDs have been combined with some commonly used computer experimental design criteria to achieve enhanced design performance. For example, the Maximin-LHDs were developed to improve its space-filling property in the full dimension of all input variables. The MaxPro-LHDs were proposed in recent years to obtain nicer projections in any subspace of input variables. This thesis integrates both space-filling and projection characteristics for LHDs and develops new algorithms for constructing optimal LHDs that achieve nice properties on both criteria based on using the Pareto front optimization approach. The new LHDs are evaluated through case studies and compared with traditional methods to demonstrate their improved performance.
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Ho, Wai Man. "Case studies in computer experiments, applications of uniform design and modern modeling techniques". HKBU Institutional Repository, 2001. http://repository.hkbu.edu.hk/etd_ra/290.
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Marin, Ofelia. "Designing computer experiments to estimate integrated response functions". Columbus, Ohio : Ohio State University, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1135206870.
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Roy, Soma. "Sequential-Adaptive Design of Computer Experiments for the Estimation of Percentiles". The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1218032995.
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Huang, Yangxin. "Interval estimation of effective doses and optimal designs for quantal response experiments". Thesis, Liverpool John Moores University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.313104.
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Ferris, Steven. "Response models and efficient designs for change-over experiments with treatment carryover". Thesis, University of Edinburgh, 1999. http://hdl.handle.net/1842/10900.
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Humans are used as recording instruments in many areas of scientific experimentation. However, their responses are susceptible to bias due to the context in which the sensory stimuli are presented. One recognised source of bias is carryover, i.e. the effect of the previous stimuli on the current judgement. It is therefore important to take account of carryover effects in both the design and analysis of the experiments in order to obtain precise and bias free estimates of experimental treatment effects. In this study we investigate carryover in two areas: sensory profiling of food products and the assessment of crop disease severity. A series of experiments are designed, conducted and analysed for both applications, in order to ascertain the form, frequency and magnitude of carryover. Alternatives to the standard additive carryover model are proposed for the sensory profiling responses. The proposed model has carryover effects which are proportional to direct treatment effects. In visual assessment carryover is found to depend on whether the previous stimulus is higher than the current stimulus and an appropriate model is developed to describe this relationship. Results for optimal and efficient change-over designs for estimating direct treatment effects in the presence of carryover, in addition to repeat treatment effects, are derived for the proportional carryover model analytically. Balanced uniform designs with or without a circular pre-period for specified design parameters are determined to be optimal within their respective classes of competing designs. The search for optimal and efficient change-over designs is extended to all possible designs using a computer search algorithm. However, the relative efficiency of designs is shown to depend on the value of the proportional scalar linking carryover effects to direct treatment effects, and knowledge of this parameter will influence the optimal design.
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Truong, David Hien. "Single-Step Factor Screening and Response Surface Optimization Using Optimal Designs with Minimal Aliasing". VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/64.
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Cheng and Wu (2001) introduced a method for response surface exploration using only one design by using a 3-level design to first screen a large number of factors and then project onto the significant factors to perform response surface exploration. Previous work generally involved selecting designs based on projection properties first and aliasing structure second. However, having good projection properties is of little concern if the correct factors cannot be identified. We apply Jones and Nachtsheim’s (2009) method for finding optimal designs with minimal aliasing to find 18, 27, and 30-run designs to use for single-step screening and optimization. Our designs have better factor screening capabilities than the designs of Cheng and Wu (2001) and Xu et al. (2004), while maintaining similar D-efficiencies and allowing all projections to fit a full second order model.
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Lin, Hefang. "One-Stage and Bayesian Two-Stage Optimal Designs for Mixture Models". Diss., Virginia Tech, 1999. http://hdl.handle.net/10919/30224.
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In this research, Bayesian two-stage D-D optimal designs for mixture experiments with or without process variables under model uncertainty are developed. A Bayesian optimality criterion is used in the first stage to minimize the determinant of the posterior variances of the parameters. The second stage design is then generated according to an optimality procedure that collaborates with the improved model from first stage data. Our results show that the Bayesian two-stage D-D optimal design is more efficient than both the Bayesian one-stage D-optimal design and the non-Bayesian one-stage D-optimal design in most cases. We also use simulations to investigate the ratio between the sample sizes for two stages and to observe least sample size for the first stage. On the other hand, we discuss D-optimal second or higher order designs, and show that Ds-optimal designs are a reasonable alternative to D-optimal designs.Ph. D.
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Schiffl, Katharina [Verfasser]. "Optimal designs for two-color microarray experiments in multi-factorial models / Katharina Schiffl". Aachen : Hochschulbibliothek der Rheinisch-Westfälischen Technischen Hochschule Aachen, 2012. http://d-nb.info/1020253266/34.
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Ang, Keng-Ern Joshua. "Extending orthogonal and nearly orthogonal Latin hypercube designs for computer simulation and experiments". Thesis, Monterey California. Naval Postgraduate School, 2006. http://hdl.handle.net/10945/2397.
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Penso, Michele Maria Signoreti. "Aplicações de delineamentos sistemáticos em experimentos agronômicos". Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-06042018-151348/.
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O objetivo do presente trabalho é analisar a eficácia de um desenho em campo e o quanto este pode facilitar a implantação, avaliação e manutenção dos experimentos, principalmente se estes necessitam de uma grande área para a instalação. O delineamento sistemático é aplicado em áreas, tais como, horticultura, solo, irrigação e vem sendo abordado nas pesquisas florestais que se preocupam em oferecer respostas aos gradientes de espaçamentos que uma espécie apresenta em campo. O trabalho, mostra uma revisão bibliográfica de vários estudos realizados com delineamento sistemático em uma gama de aplicações em distintas áreas, revelando suas dificuldades, vantagens e desvantagens sobre os chamados delineamentos clássicos. Os desenhos em campo podem seguir dois diferentes tipos de delineamentos sistemáticos, em \"leque\" ou linhas paralelas. Para cada um existem regras que devem ser seguidas para a instalação dos experimentos em campo. Com o objetivo de retratar o comportamento da espécie Eucalypus dunni, submetida a diferentes tratamentos, sendo estes os espaçamentos entre as plantas, foi constatada a vantagem em empregar o delineamento sistemático, que reduziu a área utilizada e os custos de produção, melhorando a avaliação e a sobrevivência das espécies. Esse estudo possibilitou identificar o espaçamento ideal para a instalação desta espécie em campo, por meio da análise de regressão.The objective of the present work is to analyze the effectiveness of a drawing in the field and how much it can facilitate the implantation, evaluation and maintenance of the experiments, especially if they require a large area for the installation. The systematic design is applied in areas such as horticulture, soil, irrigation and has been approached in the forest researches that are concerned with providing answers to the gradients of spacing that a species presents in the field. This work presents a bibliographical review of several studies carried out with systematic design in a range of applications in different areas, revealing their difficulties, advantages and disadvantages on the so - called classic designs. Field designs can follow two different types of systematic, fan-like or parallel lines. For each one there are rules that must be followed for the installation of field experiments. With the aim of portraying the behavior of the species Eucalypus dunni, submitted to different treatments, these being the spacings between plants, it was observed the advantage of using the systematic design, which reduced the area used and the production costs, improving the evaluation and survival of the species. This study made it possible to identify the ideal spacing for the installation of this species in the field, through regression analysis.
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O'Brien, James Scott. "The heterotopia of the city : three experiments for design in an urban environment". Thesis, Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/21744.
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Olsson, Ing-Marie. "Experimental Designs at the Crossroads of Drug Discovery". Doctoral thesis, Umeå : Department of Chemistry, Umeå University, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-693.
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Lin, Meily. "Construction of designs in the presence of polynomial trends for varietal or factorial experiments /". The Ohio State University, 1989. http://rave.ohiolink.edu/etdc/view?acc_num=osu14875987480194.
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Jangid, Anuradha. "Verifying IP-Cores by Mapping Gate to RTL-Level Designs". Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1385975878.
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Leonard, Robert D. "Considerations for Screening Designs and Follow-Up Experimentation". VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/3928.
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The success of screening experiments hinges on the effect sparsity assumption, which states that only a few of the factorial effects of interest actually have an impact on the system being investigated. The development of a screening methodology to harness this assumption requires careful consideration of the strengths and weaknesses of a proposed experimental design in addition to the ability of an analysis procedure to properly detect the major influences on the response. However, for the most part, screening designs and their complementing analysis procedures have been proposed separately in the literature without clear consideration of their ability to perform as a single screening methodology.
As a contribution to this growing area of research, this dissertation investigates the pairing of non-replicated and partially–replicated two-level screening designs with model selection procedures that allow for the incorporation of a model-independent error estimate. Using simulation, we focus attention on the ability to screen out active effects from a first order with two-factor interactions model and the possible benefits of using partial replication as part of an overall screening methodology. We begin with a focus on single-criterion optimum designs and propose a new criterion to create partially replicated screening designs. We then extend the newly proposed criterion into a multi-criterion framework where estimation of the assumed model in addition to protection against model misspecification are considered. This is an important extension of the work since initial knowledge of the system under investigation is considered to be poor in the cases presented. A methodology to reduce a set of competing design choices is also investigated using visual inspection of plots meant to represent uncertainty in design criterion preferences. Because screening methods typically involve sequential experimentation, we present a final investigation into the screening process by presenting simulation results which incorporate a single follow-up phase of experimentation. In this concluding work we extend the newly proposed criterion to create optimal partially replicated follow-up designs. Methodologies are compared which use different methods of incorporating knowledge gathered from the initial screening phase into the follow-up phase of experimentation.
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Nicely, Lindsey. "Applications of Sure Independence Screening Analysis for Supersaturated Designs". VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/2694.
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Experimental design has applications in many fields, from medicine to manufacturing. Incorporating statistics into both the planning and analysis stages of the experiment will ensure that appropriate data are collected to allow for meaningful analysis and interpretation of the results. If the number of factors of interest is very large, or if the experimental runs are very expensive, then a supersaturated design (SSD) can be used for factor screening. These designs have n runs and k > n - 1 factors, so there are not enough degrees of freedom to allow estimation of all of the main effects. This paper will first review some of the current techniques for the construction and analysis of SSDs, as well as the analysis challenges inherent to SSDs. Analysis techniques of Sure Independence Screening (SIS) and Iterative Sure Independence Screening (ISIS) are discussed, and their applications for SSDs are explored using simulation, in combination with the Smoothly Clipped Absolute Deviation (SCAD) approach for down-selecting and estimating the effects.
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Kessel, Lisa. "Regularities in the Augmentation of Fractional Factorial Designs". VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/2993.
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Two-level factorial experiments are widely used in experimental design because they are simple to construct and interpret while also being efficient. However, full factorial designs for many factors can quickly become inefficient, time consuming, or expensive and therefore fractional factorial designs are sometimes preferable since they provide information on effects of interest and can be performed in fewer experimental runs. The disadvantage of using these designs is that when using fewer experimental runs, information about effects of interest is sometimes lost. Although there are methods for selecting fractional designs so that the number of runs is minimized while the amount of information provided is maximized, sometimes the design must be augmented with a follow-up experiment to resolve ambiguities. Using a fractional factorial design augmented with an optimal follow-up design allows for many factors to be studied using only a small number of additional experimental runs, compared to the full factorial design, without a loss in the amount of information that can be gained about the effects of interest. This thesis looks at discovering regularities in the number of follow-up runs that are needed to estimate all aliased effects in the model of interest for 4-, 5-, 6-, and 7-factor resolution III and IV fractional factorial experiments. From this research it was determined that for all of the resolution IV designs, four or fewer (typically three) augmented runs would estimate all of the aliased effects in the model of interest. In comparison, all of the resolution III designs required seven or eight follow-up runs to estimate all of the aliased effects of interest. It was determined that D-optimal follow-up experiments were significantly better with respect to run size economy versus fold-over and semi-foldover designs for (i) resolution IV designs and (ii) designs with larger run sizes.
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Zhang, Yulei. "Computer Experiments with Both Quantitative and Qualitative Inputs". The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1408042133.
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Matthews, D. I. "Replication methods applied to issues of non independence in the designs of contingent valuation experiments". Thesis, Queen's University Belfast, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.679225.
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The author's intention in presenting this thesis is to develop new and simple to apply analytic methods to robustly estimate and compare Willingness to Pay (WTP) estimates and particularly to develop tests for statistical significance in these differences by estimating sampling variance appropriate to the method, taking into account the correlation between estimates incorporating the CV methods and or sampling designs. In particular I am interested in testing differences in estimated valuation statistics, such as Median WTP and thus overcoming aspects of non independence in the welfare estimates due to the design of the survey methodology e.g. in Double Bounded Dichotomous choices the estimate of the Double Bounded welfare estimate is not independent of the Single Bounded estimate because of the nature of the DB method. The use of replication here refers specifically to resampling methods that are used to provide variances of differences by simulating the sampling distribution by randomly resampling and thus quantifying the sampling variation. Here 3 of the 4 papers presented use either the bootstrap or jac1dmife method to produce the sampling variation of difference. The fourth paper uses a split sample approach to test individual WTP differences with a pooled Benefit Transfer estimate. The thesis presents four papers from 4 separate stated preference studies using different Contingent Valuation question formats which are used to evaluate WTP for: Improvement to Animal Welfare, Child safety on farms, Values of Forest recreation and Renewable Electricity generation in Chile. The theme of the thesis is to test for differences in estimates in WTP estimates between various within sample and across samples designs. These tests are used in 2 papers to test for the occurrences of inconsistency between SB and DB estimates and to test for anchoring of the second bid in relation to the level of the first bid offered amount in DBDC and thus to test for respondent behavioural effects such as Learning on inconsistency and anchoring.
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Liang, Li. "Graphical Tools, Incorporating Cost and Optimizing Central Composite Designs for Split-Plot Response Surface Methodology Experiments". Diss., Virginia Tech, 2005. http://hdl.handle.net/10919/26768.
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In many industrial experiments, completely randomized designs (CRDs) are impractical due to restrictions on randomization, or the existence of one or more hard-to-change factors. Under these situations, split-plot experiments are more realistic. The two separate randomizations in split-plot experiments lead to different error structure from in CRDs, and hence this affects not only response modeling but also the choice of design. In this dissertation, two graphical tools, three-dimensional variance dispersion graphs (3-D VDGs) and fractions of design space (FDS) plots are adapted for split-plot designs (SPDs). They are used for examining and comparing different variations of central composite designs (CCDs) with standard, V- and G-optimal factorial levels. The graphical tools are shown to be informative for evaluating and developing strategies for improving the prediction performance of SPDs. The overall cost of a SPD involves two types of experiment units, and often each individual whole plot is more expensive than individual subplot and measurement. Therefore, considering only the total number of observations is likely not the best way to reflect the cost of split-plot experiments. In this dissertation, cost formulation involving the weighted sum of the number of whole plots and the total number of observations is discussed and the three cost adjusted optimality criteria are proposed. The effects of considering different cost scenarios on the choice of design are shown in two examples. Often in practice it is difficult for the experimenter to select only one aspect to find the optimal design. A realistic strategy is to select a design with good balance for multiple estimation and prediction criteria. Variations of the CCDs with the best cost-adjusted performance for estimation and prediction are studied for the combination of D-, G- and V-optimality criteria and each individual criterion.Ph. D.
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Kumar, Arun. "Sequential Calibration Of Computer Models". The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1218568898.
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Pokhilko, Victoria V. "Statistical Designs for Network A/B Testing". VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/6101.
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A/B testing refers to the statistical procedure of experimental design and analysis to compare two treatments, A and B, applied to different testing subjects. It is widely used by technology companies such as Facebook, LinkedIn, and Netflix, to compare different algorithms, web-designs, and other online products and services. The subjects participating in these online A/B testing experiments are users who are connected in different scales of social networks. Two connected subjects are similar in terms of their social behaviors, education and financial background, and other demographic aspects. Hence, it is only natural to assume that their reactions to online products and services are related to their network adjacency. In this research, we propose to use the conditional autoregressive model (CAR) to present the network structure and include the network effects in the estimation and inference of the treatment effect. The following statistical designs are presented: D-optimal design for network A/B testing, a re-randomization experimental design approach for network A/B testing and covariate-assisted Bayesian sequential design for network A/B testing. The effectiveness of the proposed methods are shown through numerical results with synthetic networks and real social networks.
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Amanna, Ashwin Earl. "Statistical Experimental Design Framework for Cognitive Radio". Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/77331.
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This dissertation presents an empirical approach to identifying decisions for adapting cognitive radio parameters with no a priori knowledge of the environment. Cognitively inspired radios, attempt to combine observed metrics of system performance with artificial intelligence decision-making algorithms. Current architectures trend towards hybrid combinations of heuristics, such as genetic algorithms (GA) and experiential methods, such as case-based reasoning (CBR). A weakness in the GA is its reliance on limited mathematical models for estimating bit error rate, packet error rate, throughput, and signal-to-noise ratio. The CBR approach is similarly limited by its dependency on past experiences. Both methods have potential to suffer in environments not previously encountered. In contrast, the statistical methods identify performance estimation models based on exercising defined experimental designs. This represents an experiential decision-making process formed in the present rather than the past. There are three core contributions from this empirical framework: 1) it enables a new approach to decision making based on empirical estimation models of system performance, 2) it provides a systematic method for initializing cognitive engine configuration parameters, and 3) it facilitates deeper understanding of system behavior by quantifying parameter significance, and interaction effects. Ultimately, this understanding enables simplification of system models by identifying insignificant parameters. This dissertation defines an abstract framework that enables application of statistical approaches to cognitive radio systems regardless of its platform or application space. Specifically, it assesses factorial design of experiments and response surface methodology (RSM) to an over-the-air wireless radio link. Results are compared to a benchmark GA cognitive engine. The framework is then used for identifying software-defined radio initialization settings. Taguchi designs, a related statistical method, are implemented to identify initialization settings of a GA.Ph. D.
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Silver, Ian. "Genetically Adjusted Propensity Score Matching: A Proposal of a Novel Analytical Tool to Help Close the Gap between Non-experimental Designs and True Experiments in the Social Sciences". University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1561996445863208.
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Thalberg, Hege Grøstad. "A Comparison Study of Different Optimizing Criteria and Confounding Patterns For Multi-Level Binary Replacement and Other Designs Used in Computer Experiments". Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for matematiske fag, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-14418.
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We have constructed four different types of designs for computer experiments. Thedesign types are based on latin hypercube sampling (LHS), orthogonal arrays (OA), ran-dom sampling and the recently proposed multi-level binary replacement (MBR) design.For each type of design we have attempted to find the best possible design out of acertain number of constructed designs using three different optimizing criteria: the alias sum of square criterion (ASSC), the L-criterion and a modified A-criterion. The chosen design has then been tested by fitting an approximate model and calculating maximum error (MAX) and root mean squared error (RMSE) values. We observed that out of the three criteria applied the ASSC performed the best.In addition to comparing criteria for optimizing the design choice, we have alsoconstructed non-optimized designs for comparing the different design types and thedifferent ways of constructing MBR designs. In this setting we observed that OA designsperformed well in general, whereas the MBR designs performed well when restricted toa small number of factors.
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Pfeifer, Hannes [Verfasser], Oskar [Akademischer Betreuer] Painter e Stefan [Gutachter] Götzinger. "Silicon optomechanical crystals for arrays - tunability, disorder and 2D designs for low temperature experiments / Hannes Pfeifer ; Gutachter: Stefan Götzinger ; Betreuer: Oskar Painter". Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2018. http://d-nb.info/116695112X/34.
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Ke, Xiao. "On lower bounds of mixture L₂-discrepancy, construction of uniform design and gamma representative points with applications in estimation and simulation". HKBU Institutional Repository, 2015. https://repository.hkbu.edu.hk/etd_oa/152.
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Two topics related to the experimental design are considered in this thesis. On the one hand, the uniform experimental design (UD), a major kind of space-filling design, is widely used in applications. The majority of UD tables (UDs) with good uniformity are generated under the centralized {dollar}L_2{dollar}-discrepancy (CD) and the wrap-around {dollar}L_2{dollar}-discrepancy (WD). Recently, the mixture {dollar}L_2{dollar}-discrepancy (MD) is proposed and shown to be more reasonable than CD and WD in terms of uniformity. In first part of the thesis we review lower bounds for MD of two-level designs from a different point of view and provide a new lower bound. Following the same idea we obtain a lower bound for MD of three-level designs. Moreover, we construct UDs under the measurement of MD by the threshold accepting (TA) algorithm, and finally we attach two new UD tables with good properties derived from TA under the measurement of MD. On the other hand, the problem of selecting a specific number of representative points (RPs) to maintain as much information as a given distribution has raised attention. Previously, a method has been given to select type-II representative points (RP-II) from normal distribution. These point sets have good properties and minimize the information loss. Whereafter, following similar idea, Fu, 1985 have discussed RP-II for gamma distribution. In second part of the thesis, we improve the discussion of selecting Gamma RP-II and provide more RP-II tables with a number of parameters. Further in statistical simulation, we also evaluate the estimation performance of point sets resampled from Gamma RP-II by making comparison in different situations.
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Montenon, Alaric. "Analyse, mutualisation et optimisation par la commande de la consommation énergétique des héliostats autonomes des centrales à concentration solaire". Phd thesis, Toulouse, INPT, 2013. http://oatao.univ-toulouse.fr/9709/1/montenon_partie_1_sur_2.pdf.
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Les centrales à concentration utilisent la radiation solaire directe pour produire de la chaleur et, comme dans la plupart des cas, de l’électricité par transformation de cette chaleur. Les centrales dites « à tour » sont associées à un vaste champ héliostatique dont l’alimentation se fait généralement par de longs câbles à l’intérieur de tranchées qui peuvent mesurer plusieurs kilomètres. Pour pallier cette solution, la thèse s’inscrit donc dans l’évaluation de la mise en place d’héliostats autonomes énergétiquement via un générateur solaire et un stockage électrochimique (batteries). Le dimensionnement de ce système est optimisé par la commande des deux moteurs des héliostats (azimut et élévation) en vue de minimiser leur consommation globale. L’impact environnemental global est également comparé par rapport à la solution des tranchées grâce à une analyse de cycle de vie. Cette recherche est effectuée au sein du laboratoire LAPLACE de Toulouse en collaboration avec le laboratoire PROMES d’Odeillo.