Relevant bibliographies by topics / Sequential design of experiments

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Sequential design of experiments'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 February 2022

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Journal articles on the topic "Sequential design of experiments"

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Durrieu, Gilles, and Laurent Briollais. "Sequential Design for Microarray Experiments." Journal of the American Statistical Association 104, no. 486 (June 2009): 650–60. http://dx.doi.org/10.1198/jasa.2009.0135.

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Lewi, Jeremy, Robert Butera, and Liam Paninski. "Sequential Optimal Design of Neurophysiology Experiments." Neural Computation 21, no. 3 (March 2009): 619–87. http://dx.doi.org/10.1162/neco.2008.08-07-594.

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Adaptively optimizing experiments has the potential to significantly reduce the number of trials needed to build parametric statistical models of neural systems. However, application of adaptive methods to neurophysiology has been limited by severe computational challenges. Since most neurons are high-dimensional systems, optimizing neurophysiology experiments requires computing high-dimensional integrations and optimizations in real time. Here we present a fast algorithm for choosing the most informative stimulus by maximizing the mutual information between the data and the unknown parameters of a generalized linear model (GLM) that we want to fit to the neuron's activity. We rely on important log concavity and asymptotic normality properties of the posterior to facilitate the required computations. Our algorithm requires only low-rank matrix manipulations and a two-dimensional search to choose the optimal stimulus. The average running time of these operations scales quadratically with the dimensionality of the GLM, making real-time adaptive experimental design feasible even for high-dimensional stimulus and parameter spaces. For example, we require roughly 10 milliseconds on a desktop computer to optimize a 100-dimensional stimulus. Despite using some approximations to make the algorithm efficient, our algorithm asymptotically decreases the uncertainty about the model parameters at a rate equal to the maximum rate predicted by an asymptotic analysis. Simulation results show that picking stimuli by maximizing the mutual information can speed up convergence to the optimal values of the parameters by an order of magnitude compared to using random (nonadaptive) stimuli. Finally, applying our design procedure to real neurophysiology experiments requires addressing the nonstationarities that we would expect to see in neural responses; our algorithm can efficiently handle both fast adaptation due to spike history effects and slow, nonsystematic drifts in a neuron's activity.
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Yu, Xiaoli, Jiahua Chen, and Rollin Brant. "Sequential design for binary dose–response experiments." Journal of Statistical Planning and Inference 177 (October 2016): 64–73. http://dx.doi.org/10.1016/j.jspi.2016.04.005.

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Yubin, Tian, and Fang Yongfei. "An efficient sequential design for sensitivity experiments." Acta Mathematica Scientia 30, no. 1 (January 2010): 269–80. http://dx.doi.org/10.1016/s0252-9602(10)60044-6.

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Tsitovich, I. I. "Sequential Design of Experiments for Hypothesis Testing." Theory of Probability & Its Applications 29, no. 4 (January 1985): 814–17. http://dx.doi.org/10.1137/1129109.

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Tian, Yubin, Yongfei Fang, and Dianpeng Wang. "Sequential empirical Bayesian design for sensitivity experiments." Journal of Systems Science and Complexity 24, no. 5 (October 2011): 955–68. http://dx.doi.org/10.1007/s11424-011-8122-4.

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Kanninen, Barbara J. "Design of Sequential Experiments for Contingent Valuation Studies." Journal of Environmental Economics and Management 25, no. 1 (July 1993): S1—S11. http://dx.doi.org/10.1006/jeem.1993.1029.

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Rieder, Ulrich, and Hartmut Wagner. "Structured policies in the sequential design of experiments." Annals of Operations Research 32, no. 1 (December 1991): 165–88. http://dx.doi.org/10.1007/bf02204833.

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Kleijnen, Jack P. C., and Wen Shi. "Sequential probability ratio tests: conservative and robust." SIMULATION 97, no. 1 (September 30, 2020): 33–43. http://dx.doi.org/10.1177/0037549720954916.

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Because computers (except for parallel computers) generate simulation outputs sequentially, we recommend sequential probability ratio tests (SPRTs) for the statistical analysis of these outputs. However, until now simulation analysts have ignored SPRTs. To change this situation, we review SPRTs for the simplest case; namely, the case of choosing between two hypothesized values for the mean simulation output. For this case, the classic SPRT of Wald (Wald A. Sequential tests of statistical hypotheses. Ann Math Stat 1945; 16: 117–186) allows general types of distribution, including normal distributions with known variances. A modification permits unknown variances that are estimated. Hall (Hall WJ. Some sequential analogs of Stein’s two-stage test. Biometrika 1962; 49: 367–378) developed a SPRT that assumes normal distributions with unknown variances estimated from a pilot sample. A modification uses a fully sequential variance estimator. In this paper, we quantify the performance of the various SPRTs, using several Monte Carlo experiments. In experiment #1, simulation outputs are normal. Whereas Wald’s SPRT with estimated variance gives too high error rates, Hall’s original and modified SPRTs are “conservative”; that is, the actual error rates are smaller than those prespecified (nominal). Furthermore, our experiment shows that the most efficient SPRT is Hall’s modified SPRT. In experiment #2, we estimate the robustness of these SPRTs for non-normal output. For these two experiments, we provide details on their design and analysis; these details may also be useful for simulation experiments in general.
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Lin, Zheng-yan, and Li-xin Zhang. "Adaptive designs for sequential experiments." Journal of Zhejiang University-SCIENCE A 4, no. 2 (March 2003): 214–20. http://dx.doi.org/10.1631/jzus.2003.0214.

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Dissertations / Theses on the topic "Sequential design of experiments"

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Gupta, Abhishek. "Robust design using sequential computer experiments." Thesis, Texas A&M University, 2004. http://hdl.handle.net/1969.1/492.

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Modern engineering design tends to use computer simulations such as Finite Element Analysis (FEA) to replace physical experiments when evaluating a quality response, e.g., the stress level in a phone packaging process. The use of computer models has certain advantages over running physical experiments, such as being cost effective, easy to try out different design alternatives, and having greater impact on product design. However, due to the complexity of FEA codes, it could be computationally expensive to calculate the quality response function over a large number of combinations of design and environmental factors. Traditional experimental design and response surface methodology, which were developed for physical experiments with the presence of random errors, are not very effective in dealing with deterministic FEA simulation outputs. In this thesis, we will utilize a spatial statistical method (i.e., Kriging model) for analyzing deterministic computer simulation-based experiments. Subsequently, we will devise a sequential strategy, which allows us to explore the whole response surface in an efficient way. The overall number of computer experiments will be remarkably reduced compared with the traditional response surface methodology. The proposed methodology is illustrated using an electronic packaging example.
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Lewi, Jeremy. "Sequential optimal design of neurophysiology experiments." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28201.

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Thesis (M. S.)--Biomedical Engineering, Georgia Institute of Technology, 2009.
Committee Co-Chair: Butera, Robert; Committee Co-Chair: Paninski, Liam; Committee Member: Isbell, Charles; Committee Member: Rozell, Chris; Committee Member: Stanley, Garrett; Committee Member: Vidakovic, Brani.
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Koita, Rizwan R. (Rizwan Rahim). "Strategies for sequential design of experiments." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/35998.

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Wang, Hungjen 1971. "Sequential optimization through adaptive design of experiments." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/39332.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2007.
Includes bibliographical references (p. 111-118).
This thesis considers the problem of achieving better system performance through adaptive experiments. For the case of discrete design space, I propose an adaptive One-Factor-at-A-Time (OFAT) experimental design, study its properties and compare its performance to saturated fractional factorial designs. The rationale for adopting the adaptive OFAT design scheme become clear if it is imbedded in a Bayesian framework: it becomes clear that OFAT is an efficient response to step by step accrual of sample information. The Bayesian predictive distribution for the outcome by implementing OFAT and the corresponding principal moments when a natural conjugate prior is assigned to parameters that are not known with certainty are also derived. For the case of compact design space, I expand the treatment of OFAT by the removal of two restrictions imposed on the discrete design space. The first is that the selection of input level at each iteration depends only on observed best response and does not depend on other prior information. In most real cases, domain experts possess knowledge about the process being modeled that, ideally, should be treated as sample information in its own right-and not simply ignored.
(cont.) Treating the design problem Bayesianly provides a logical scheme for incorporation of expert information. The second removed restriction is that the model is restricted to be linear with pair-wise interactions - implying that the model considers a relatively small design space. I extend the Bayesian analysis to the case of generalized normal linear regression model within the compact design space. With the concepts of c-optimum experimental design and Bayesian estimations, I propose an algorithm for the purpose of achieving optimum through a sequence of experiments. I prove that the proposed algorithm would generate a consistent Bayesian estimator in its limiting behavior. Moreover, I also derive the expected step-wise improvement achieved by this algorithm for the analysis of its intermediate behavior, a critical criterion for determining whether to continue the experiments.
by Hungjen Wang.
Ph.D.
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Lehman, Jeffrey S. "Sequential Design of Computer Experiments for Robust Parameter Design." The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1027963706.

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Lehman, Jeffrey Scott. "Sequential design of computer experiments for robust parameter design." The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1486463321623652.

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Yu, Xiaoli. "Sequential ED-design for binary dose-response experiments." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/63447.

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Dose-response experiments and subsequent data analyses are often carried out according to optimal designs for the purpose of accurately determining a specific effective dose (ED) level. If the interest is the dose-response relationship over a range of ED levels, many existing optimal designs are not accurate. In this dissertation, we propose a new design procedure, called two-stage sequential ED-design, which directly and simultaneously targets several ED levels. We use a small number of trials to provide a tentative estimation of the model parameters. The doses of the subsequent trials are then selected sequentially, based on the latest model information, to maximize the efficiency of the ED estimation over several ED levels. Although the commonly used logistic and probit models are convenient summaries of the dose-response relationship, they can be too restrictive. We introduce and study a more flexible albeit slightly more complex three-parameter logistic dose-response model. We explore the effectiveness of the sequential ED-design and the D-optimal design under this model, and develop an effective model fitting strategy. We develop a two-step iterative algorithm to compute the maximum likelihood estimate of the model parameters. We prove that the algorithm iteration increases the likelihood value, and therefore will lead to at least a local maximum of the likelihood function. We also study the numerical solution to the D-optimal design for the three-parameter logistic model. Interestingly, all our numerical solutions to the D-optimal design are three-point-support distributions. We also discuss the use of the ED-design when experimental subjects become available in groups. We introduce the group sequential ED-design, and demonstrate how to construct this design. The ED-design has a natural extension to more complex model and can satisfy a broad range of the demands that may arise in applications.
Science, Faculty of
Statistics, Department of
Graduate
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Li, Ling. "Sequential Design of Experiments to Estimate a Probability of Failure." Phd thesis, Supélec, 2012. http://tel.archives-ouvertes.fr/tel-00765457.

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This thesis deals with the problem of estimating the probability of failure of a system from computer simulations. When only an expensive-to-simulate model of the system is available, the budget for simulations is usually severely limited, which is incompatible with the use of classical Monte Carlo methods. In fact, estimating a small probability of failure with very few simulations, as required in some complex industrial problems, is a particularly difficult topic. A classical approach consists in replacing the expensive-to-simulate model with a surrogate model that will use little computer resources. Using such a surrogate model, two operations can be achieved. The first operation consists in choosing a number, as small as possible, of simulations to learn the regions in the parameter space of the system that will lead to a failure of the system. The second operation is about constructing good estimators of the probability of failure. The contributions in this thesis consist of two parts. First, we derive SUR (stepwise uncertainty reduction) strategies from a Bayesian-theoretic formulation of the problem of estimating a probability of failure. Second, we propose a new algorithm, called Bayesian Subset Simulation, that takes the best from the Subset Simulation algorithm and from sequential Bayesian methods based on Gaussian process modeling. The new strategies are supported by numerical results from several benchmark examples in reliability analysis. The methods proposed show good performances compared to methods of the literature.
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Williams, Brian J. "Sequential design of computer experiments to minimize integrated response functions /." The Ohio State University, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488203158826046.

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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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Books on the topic "Sequential design of experiments"

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1937-, Fristedt Bert, ed. Bandit problems: Sequential allocation of experiments. London: Chapman and Hall, 1985.

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Caragiu, Mihai. Sequential Experiments with Primes. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56762-4.

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Bhattacharyya, Asok. Checking experiments in sequential machines. New York: Wiley, 1989.

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Roberts, E. A. Sequential Data in Biological Experiments. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-3120-9.

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Ransbotham, Sam. Sequential grid computing: Models and computational experiments. Bangalore: Indian Institute of Management Bangalore, 2009.

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Ashar, Pranav. Sequential Logic Synthesis. Boston, MA: Springer US, 1992.

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Srinivas, Devadas, and Newton A. Richard 1951-, eds. Sequential logic testing and verification. Boston: Kluwer Academic, 1992.

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George, Leinonen, ed. Programmable controllers & designing sequential logic. Ft. Worth: Saunders College Pub., 1992.

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Loch, C. H. Parallel and sequential testing of design alternatives. Fontainebleau: INSEAD, 1999.

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Ashar, Pranav. Sequential logic synthesis. Boston: Kluwer Academic Publishers, 1992.

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Book chapters on the topic "Sequential design of experiments"

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Chernoff, Herman. "Sequential Design of Experiments." In Springer Series in Statistics, 345–60. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-4380-9_27.

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Roberts, E. A. "Pre-treatment observations in the design of experiments." In Sequential Data in Biological Experiments, 150–62. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-3120-9_6.

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Balasko, B., J. Madar, and J. Abonyi. "Additive Sequential Evolutionary Design of Experiments." In Artificial Intelligence and Soft Computing – ICAISC 2006, 324–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11785231_35.

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Kitsos, Christos P. "Sequential Designs." In Optimal Experimental Design for Non-Linear Models, 39–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45287-1_5.

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Williams, Brian J., Thomas J. Santner, William I. Notz, and Jeffrey S. Lehman. "Sequential Design of Computer Experiments for Constrained Optimization." In Statistical Modelling and Regression Structures, 449–72. Heidelberg: Physica-Verlag HD, 2009. http://dx.doi.org/10.1007/978-3-7908-2413-1_24.

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Robbins, Herbert. "Some Aspects of the Sequential Design of Experiments." In Herbert Robbins Selected Papers, 169–77. New York, NY: Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4612-5110-1_13.

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Sen, P. K. "Introduction to Chernoff (1959) Sequential Design of Experiments." In Springer Series in Statistics, 339–44. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-4380-9_26.

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Girlich, Hans-Joachim, and Siegmar Pohlenz. "Some Sequential Designs for Bernoulli Experiments." In Operations Research Proceedings 1993, 538. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-78910-6_185.

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Kaptein, Maurits. "Experiments, Longitudinal Studies, and Sequential Experimentation: How Using “Intermediate” Results Can Help Design Experiments." In Human–Computer Interaction Series, 121–49. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67322-2_7.

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Slud, Eric V. "Adaptive Designs for Group Sequential Clinical Survival Experiments." In Statistical Models and Methods for Biomedical and Technical Systems, 447–59. Boston, MA: Birkhäuser Boston, 2008. http://dx.doi.org/10.1007/978-0-8176-4619-6_31.

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Conference papers on the topic "Sequential design of experiments"

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El Abiad, Hassan, Laurent Le Brusquet, Morgan Roger, and Marie-Eve Davoust. "Model-Robust Sequential Design of Experiments for Identification Problems." In 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.366267.

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Gang Lei, K. R. Shao, G. Y. Yang, Youguang Guo, Jianguo Zhu, and J. D. Lavers. "Sequential design of experiments techniques for the optimization design of electromagnetic devices." In 2010 14th Biennial IEEE Conference on Electromagnetic Field Computation (CEFC 2010). IEEE, 2010. http://dx.doi.org/10.1109/cefc.2010.5481481.

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Farhang-Mehr, Ali, and Shapour Azarm. "A Sequential Information-Theoretic Approach to Design of Computer Experiments." In 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-5571.

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Rangarajan, R., R. Raich, and A. O. Hero. "Sequential design of experiments for a rayleigh inverse scattering problem." In 2005 Microwave Electronics: Measurements, Identification, Applications. IEEE, 2005. http://dx.doi.org/10.1109/ssp.2005.1628670.

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Busby, D., and M. Feraille. "Dynamic Data Assimilation by MCMC and Sequential Design of Experiments." In 11th European Conference on the Mathematics of Oil Recovery. Netherlands: EAGE Publications BV, 2008. http://dx.doi.org/10.3997/2214-4609.20146416.

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Alfaro-Isac, C., A. Juan-Alejandre, and S. Izquierdo. "Tensor-Decomposition-Based Sequential Design of Experiments for Computer Simulations." In 10th International Conference on Adaptative Modeling and Simulation. CIMNE, 2021. http://dx.doi.org/10.23967/admos.2021.076.

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Rai, Rahul, and Matthew I. Campbell. "Qualitative and Quantitative Sequential Sampling." In ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/detc2006-99178.

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This paper introduces a method for sequentially determining experiments in a “design of experiments” where optimization and user knowledge are used to guide the efficient choice of sample points. Typical approaches to the design of experiments involves determining the sample points all at once prior to any experimentation, or sequentially based on the results of previous sample points. This method combines information from multiple fidelity sources including actual physical experiment, computer simulation models of the product, first principals involved in design and designer’s qualitative intuitions about the design. Both quantitative and qualitative information from different sources are merged together to arrive at new sampling strategy. This is accomplished by introducing the concept of confidence, C, which is represented as a field that is a function of the decision variables, x, and the performance parameter, f. The advantages of the approach are demonstrated using different example cases.
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El Abiad, Hassan, Laurent Le Brusquet, and Marie-Eve Davoust. "Model-robust Design of Experiments for sequential identification of ODE parameters." In 2008 IEEE Workshop on Machine Learning for Signal Processing (MLSP) (Formerly known as NNSP). IEEE, 2008. http://dx.doi.org/10.1109/mlsp.2008.4685516.

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Lin, Yao, Farrokh Mistree, Janet K. Allen, Kwok-Leung Tsui, and Victoria C. P. Chen. "A Sequential Exploratory Experimental Design Method: Development of Appropriate Empirical Models in Design." In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/detc2004-57527.

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Much of today’s engineering analysis work consists of running complex computer codes (simulation programs), in which a vector of responses are obtained when values of design variables are supplied. To save time and effort in simulation, sampling (design of experiments) techniques are applied to help develop metamodels (empirical models or surrogate models) that can be used to replace the expensive simulations in future design stages. The usage of metamodels also helps designers to integrate inter-disciplinary codes and grasp the relationship between inputs and outputs. In this paper, we focus on a very important topic in studies of sampling and metamodeling techniques, i.e., the sequential design of experiments and metamodeling; the research question is: How to design sequential computer experiments to get accurate metamodels? After discussion of design and metamodeling strategies, a Sequential Exploratory Experimental Design (SEED) method is developed to help identify data points at different stages in metamodeling. Given limited resources, it is expected that more accurate metamodels can be developed with SEED. A single-variable example is used to help illustrate the SEED method.
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Kartik, Dhruva, Ashutosh Nayyar, and Urbashi Mitra. "Sequential Experiment Design for Hypothesis Verification." In 2018 52nd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2018. http://dx.doi.org/10.1109/acssc.2018.8645357.

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Reports on the topic "Sequential design of experiments"

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Anderson-Cook, Christine Michaela. Sequential Design of Experiments. Office of Scientific and Technical Information (OSTI), June 2017. http://dx.doi.org/10.2172/1367830.

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Anderson-Cook, Christine, Brenda Ng, Pedro Sotorrio, Abby Nachtsheim, Miranda Martin, Alex Dowling, Jialu Wong, Josh Morgan, and Charles Tong. Subtask 3.1: Sequential Design of Experiments. Office of Scientific and Technical Information (OSTI), January 2021. http://dx.doi.org/10.2172/1764858.

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Woodroofe, Michael. Topics in the Sequential Design of Experiments. Fort Belvoir, VA: Defense Technical Information Center, March 1992. http://dx.doi.org/10.21236/ada249799.

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Kupresanin, A. M., and G. Johannesson. Comparison of Sequential Designs of Computer Experiments in High Dimensions. Office of Scientific and Technical Information (OSTI), July 2011. http://dx.doi.org/10.2172/1116903.

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Chong, Philip. Integration of Physical Design and Sequential Optimization. Fort Belvoir, VA: Defense Technical Information Center, March 2006. http://dx.doi.org/10.21236/ada603903.

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Chang, Quan-Ming, and Demetrios Kazakos. Design and Analysis of Multi-Sensor Sequential Detection System. Fort Belvoir, VA: Defense Technical Information Center, July 1991. http://dx.doi.org/10.21236/ada238012.

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Wu, C. F. J. Asymptotic Inference from Sequential Design in a Nonlinear Situation. Fort Belvoir, VA: Defense Technical Information Center, August 1985. http://dx.doi.org/10.21236/ada160970.

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Cushing, Michael. Mathematica Templates for Sequential Reliability/Maintainability Test Design and Analysis. Fort Belvoir, VA: Defense Technical Information Center, June 2001. http://dx.doi.org/10.21236/ada396108.

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Lee, Yau-Hwang. Computer Aided Design for Fluidic Sequential Circuits of Fundamental Mode. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.2388.

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Laug, Kristi K. Solar Thermal Propulsion Experiments Design. Fort Belvoir, VA: Defense Technical Information Center, January 1996. http://dx.doi.org/10.21236/ada411920.

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