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Journal articles on the topic 'Sequential design of experiments'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Raghavarao, Damaraju, and James B. Wiley. "Design strategies for sequential choice experiments involving economic alternatives." Journal of Statistical Planning and Inference 136, no. 9 (September 2006): 3287–306. http://dx.doi.org/10.1016/j.jspi.2004.12.006.

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12

Kong, Xiangshun, Mingyao Ai, and Kwok Leung Tsui. "Design for Sequential Follow-Up Experiments in Computer Emulations." Technometrics 60, no. 1 (April 28, 2017): 61–69. http://dx.doi.org/10.1080/00401706.2016.1258010.

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13

Binois, Mickaël, Jiangeng Huang, Robert B. Gramacy, and Mike Ludkovski. "Replication or Exploration? Sequential Design for Stochastic Simulation Experiments." Technometrics 61, no. 1 (September 12, 2018): 7–23. http://dx.doi.org/10.1080/00401706.2018.1469433.

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14

Joo, Mingyu, Michael L. Thompson, and Greg M. Allenby. "Optimal Product Design by Sequential Experiments in High Dimensions." Management Science 65, no. 7 (July 2019): 3235–54. http://dx.doi.org/10.1287/mnsc.2018.3088.

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15

Kolonko, Michael, and Harald Benzing. "The Sequential Design of Bernoulli Experiments Including Switching Costs." Operations Research 33, no. 2 (April 1985): 412–26. http://dx.doi.org/10.1287/opre.33.2.412.

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16

Moffat, Hayden, Markus Hainy, Nikos E. Papanikolaou, and Christopher Drovandi. "Sequential experimental design for predator–prey functional response experiments." Journal of The Royal Society Interface 17, no. 166 (May 2020): 20200156. http://dx.doi.org/10.1098/rsif.2020.0156.

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Understanding functional response within a predator–prey dynamic is a cornerstone for many quantitative ecological studies. Over the past 60 years, the methodology for modelling functional response has gradually transitioned from the classic mechanistic models to more statistically oriented models. To obtain inferences on these statistical models, a substantial number of experiments need to be conducted. The obvious disadvantages of collecting this volume of data include cost, time and the sacrificing of animals. Therefore, optimally designed experiments are useful as they may reduce the total number of experimental runs required to attain the same statistical results. In this paper, we develop the first sequential experimental design method for predator–prey functional response experiments. To make inferences on the parameters in each of the statistical models we consider, we use sequential Monte Carlo, which is computationally efficient and facilitates convenient estimation of important utility functions. It provides coverage of experimental goals including parameter estimation, model discrimination as well as a combination of these. The results of our simulation study illustrate that for predator–prey functional response experiments sequential design outperforms static design for our experimental goals. R code for implementing the methodology is available via https://github.com/haydenmoffat/sequential_design_for_predator_prey_experiments .

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17

Sniderman, Paul M. "Some Advances in the Design of Survey Experiments." Annual Review of Political Science 21, no. 1 (May 11, 2018): 259–75. http://dx.doi.org/10.1146/annurev-polisci-042716-115726.

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This article calls attention to some designs in survey experiments that give new leverage in hypothesis testing and validation. The premise of this review is the modesty of survey experiments—modesty of treatment, modesty of scale, modesty of measurement. The focus of this review, accordingly, is the compensating virtues of modesty. With respect to hypothesis testing, I spotlight ( a) cross-category comparisons, ( b) null-by-design experiments, ( c) explication, ( d) conjoint designs, and ( e) sequential factorials. With respect to validation regimes, I discuss ( a) parallel studies, ( b) paired designs, and ( c) splicing. Throughout, the emphasis is on moving from experiment in the singular to experiments in the plural, learning as you go.

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18

Saluja and Dandapani. "Testable Design of Single-Output Sequential Machines Using Checking Experiments." IEEE Transactions on Computers C-35, no. 7 (July 1986): 658–62. http://dx.doi.org/10.1109/tc.1986.1676811.

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19

Xiong, F., Y. Xiong, W. Chen, and S. Yang. "Optimizing Latin hypercube design for sequential sampling of computer experiments." Engineering Optimization 41, no. 8 (July 15, 2009): 793–810. http://dx.doi.org/10.1080/03052150902852999.

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20

Lalley, S. P., and G. Lorden. "A Control Problem Arising in the Sequential Design of Experiments." Annals of Probability 14, no. 1 (January 1986): 136–72. http://dx.doi.org/10.1214/aop/1176992620.

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21

Doví, V. G., A. P. Reverberi, and L. Maga. "Optimal design of sequential experiments for error-in-variables models." Computers & Chemical Engineering 17, no. 1 (January 1993): 111–15. http://dx.doi.org/10.1016/0098-1354(93)80008-b.

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22

Pázman, Andrej. "Sequential and iterative designs of experiments." Banach Center Publications 16, no. 1 (1985): 443–53. http://dx.doi.org/10.4064/-16-1-443-453.

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23

Loeppky, Jason L., Leslie M. Moore, and Brian J. Williams. "Batch sequential designs for computer experiments." Journal of Statistical Planning and Inference 140, no. 6 (June 2010): 1452–64. http://dx.doi.org/10.1016/j.jspi.2009.12.004.

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24

Sinha, Sanjoy K., and Xiaojian Xu. "Sequential designs for repeated–measures experiments." Journal of Statistical Theory and Practice 10, no. 3 (May 4, 2016): 497–514. http://dx.doi.org/10.1080/15598608.2016.1184111.

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25

Wang, Zhenyu, Hana Sheikh, Kyongbum Lee, and Christos Georgakis. "Sequential Parameter Estimation for Mammalian Cell Model Based on In Silico Design of Experiments." Processes 6, no. 8 (July 24, 2018): 100. http://dx.doi.org/10.3390/pr6080100.

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Due to the complicated metabolism of mammalian cells, the corresponding dynamic mathematical models usually consist of large sets of differential and algebraic equations with a large number of parameters to be estimated. On the other hand, the measured data for estimating the model parameters are limited. Consequently, the parameter estimates may converge to a local minimum far from the optimal ones, especially when the initial guesses of the parameter values are poor. The methodology presented in this paper provides a systematic way for estimating parameters sequentially that generates better initial guesses for parameter estimation and improves the accuracy of the obtained metabolic model. The model parameters are first classified into four subsets of decreasing importance, based on the sensitivity of the model’s predictions on the parameters’ assumed values. The parameters in the most sensitive subset, typically a small fraction of the total, are estimated first. When estimating the remaining parameters with next most sensitive subset, the subsets of parameters with higher sensitivities are estimated again using their previously obtained optimal values as the initial guesses. The power of this sequential estimation approach is illustrated through a case study on the estimation of parameters in a dynamic model of CHO cell metabolism in fed-batch culture. We show that the sequential parameter estimation approach improves model accuracy and that using limited data to estimate low-sensitivity parameters can worsen model performance.

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26

Bect, Julien, François Bachoc, and David Ginsbourger. "A supermartingale approach to Gaussian process based sequential design of experiments." Bernoulli 25, no. 4A (November 2019): 2883–919. http://dx.doi.org/10.3150/18-bej1074.

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27

Dovi, Vincenzo G., Andrea P. Reverberi, and Leonardo Acevedo-Duarte. "New procedure for optimal design of sequential experiments in kinetic models." Industrial & Engineering Chemistry Research 33, no. 1 (January 1994): 62–68. http://dx.doi.org/10.1021/ie00025a009.

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28

Vining, Geoff. "Technical Advice: Design of Experiments, Response Surface Methodology, and Sequential Experimentation." Quality Engineering 23, no. 2 (March 2, 2011): 217–20. http://dx.doi.org/10.1080/15226514.2011.555280.

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29

Chipman, Hugh, Pritam Ranjan, and Weiwei Wang. "Sequential design for computer experiments with a flexible Bayesian additive model." Canadian Journal of Statistics 40, no. 4 (October 18, 2012): 663–78. http://dx.doi.org/10.1002/cjs.11156.

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30

Muteki, Koji, and John F. MacGregor. "Sequential design of mixture experiments for the development of new products." Journal of Chemometrics 21, no. 10-11 (2007): 496–505. http://dx.doi.org/10.1002/cem.1078.

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31

Aslett, Robert, Robert J. Buck, Steven G. Duvall, Jerome Sacks, and William J. Welch. "Circuit optimization via sequential computer experiments: design of an output buffer." Journal of the Royal Statistical Society: Series C (Applied Statistics) 47, no. 1 (January 6, 2002): 31–48. http://dx.doi.org/10.1111/1467-9876.00096.

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32

del Castillo, Enrique, and Eduardo Santiago. "A matrix-T approach to the sequential design of optimization experiments." IIE Transactions 43, no. 1 (October 29, 2010): 54–68. http://dx.doi.org/10.1080/0740817x.2010.504687.

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33

Nabifar, A., N. T. McManus, E. Vivaldo-Lima, and A. Penlidis. "A Sequential Iterative Scheme for Design of Experiments in Complex Polymerizations." Chemical Engineering & Technology 33, no. 11 (October 25, 2010): 1814–24. http://dx.doi.org/10.1002/ceat.201000237.

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34

Choi, Uihwan, Juseong Lee, Hakyoon Song, Taehyun Sung, Gisu Park, and Jeamyung Ahn. "A Study on Adaptive Design of Experiment for Sequential Free-fall Experiments in a Shock Tunnel." Journal of the Korean Society for Aeronautical & Space Sciences 46, no. 10 (October 31, 2018): 798–805. http://dx.doi.org/10.5139/jksas.2018.46.10.798.

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35

Alpizar-Ramos, Socorro, and Mario González-de la Parra. "Application of Sequential Design of Experiments to Develop Ibuprofen (400 mg) Tablets by Direct Compression." Asian Journal of Chemistry and Pharmaceutical Sciences 2, no. 1 (April 17, 2017): 10. http://dx.doi.org/10.18311/ajcps/2017/15496.

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A strategy based on sequential design of experiments (screening, optimization and confirmation) was used to develop a tablet formulation of ibuprofen (400 mg) that is manufactured by direct compression. This formulation has a high content of ibuprofen (76%), in spite of the poor flowability of the drug substance. Sequential design of experiments proved to be an effective and efficient strategy in formulation development.

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36

Beatty, C. D., and D. W. Franks. "Discriminative predation: Simultaneous and sequential encounter experiments." Current Zoology 58, no. 4 (August 1, 2012): 649–57. http://dx.doi.org/10.1093/czoolo/58.4.649.

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Abstract There are many situations in which the ability of animals to distinguish between two similar looking objects can have significant selective consequences. For example, the objects that require discrimination may be edible versus defended prey, predators versus non-predators, or mates of varying quality. Working from the premise that there are situations in which discrimination may be more or less successful, we hypothesized that individuals find it more difficult to distinguish between stimuli when they encounter them sequentially rather than simultaneously. Our study has wide biological and psychological implications from the perspective of signal perception, signal evolution, and discrimination, and could apply to any system where individuals are making relative judgments or choices between two or more stimuli or signals. While this is a general principle that might seem intuitive, it has not been experimentally tested in this context, and is often not considered in the design of models or experiments, or in the interpretation of a wide range of studies. Our study is different from previous studies in psychology in that a) the level of similarity of stimuli are gradually varied to obtain selection gradients, and b) we discuss the implications of our study for specific areas in ecology, such as the level of perfection of mimicry in predator-prey systems. Our experiments provide evidence that it is indeed more difficult to distinguish between stimuli – and to learn to distinguish between stimuli – when they are encountered sequentially rather than simultaneously, even if the intervening time interval is short.

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37

Lohr, Sharon L., and Xiaoshu Zhu. "Randomized Sequential Individual Assignment in Social Experiments: Evaluating the Design Options Prospectively." Sociological Methods & Research 46, no. 4 (December 27, 2015): 1049–75. http://dx.doi.org/10.1177/0049124115621332.

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Many randomized experiments in the social sciences allocate subjects to treatment arms at the time the subjects enroll. Desirable features of the mechanism used to assign subjects to treatment arms are often (1) equal numbers of subjects in intervention and control arms, (2) balanced allocation for population subgroups and across covariates, (3) ease of use, and (4) inability for a site worker to predict the treatment arm for a subject before he or she has been assigned. In general, a trade-off must be made among these features: Many mechanisms that achieve high balance do so at the cost of high predictability. In this article, we review methods for randomized assignment of individuals that have been discussed in the literature, evaluating the performance of each with respect to the desirable design features. We propose a method for controlling the amount of predictability in a study while achieving high balance across subgroups and covariates. The method is applicable when a database containing the subgroup membership and covariates of each potential participant is available in advance. We use simple simulation and graphical methods to evaluate the balance and predictability of randomization mechanisms when planning the study and describe a computer program implemented in the R statistical software package that prospectively evaluates candidate randomization methods.

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38

Sinsbeck, Michael, and Wolfgang Nowak. "Sequential Design of Computer Experiments for the Solution of Bayesian Inverse Problems." SIAM/ASA Journal on Uncertainty Quantification 5, no. 1 (January 2017): 640–64. http://dx.doi.org/10.1137/15m1047659.

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39

Christen, J. Andrés, and Bruno Sansó. "Advances in the Sequential Design of Computer Experiments Based on Active Learning." Communications in Statistics - Theory and Methods 40, no. 24 (December 15, 2011): 4467–83. http://dx.doi.org/10.1080/03610920903518848.

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40

Pardo, L., and M. L. Menéndez. "Informational energy in the sequential design of experiments in a Bayesian context." Information Sciences 64, no. 3 (October 1992): 271–83. http://dx.doi.org/10.1016/0020-0255(92)90104-g.

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41

Sinsbeck, Michael, Emily Cooke, and Wolfgang Nowak. "Sequential Design of Computer Experiments for the Computation of Bayesian Model Evidence." SIAM/ASA Journal on Uncertainty Quantification 9, no. 1 (January 2021): 260–79. http://dx.doi.org/10.1137/20m1320432.

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42

Li, Peng-Fei, Min-Qian Liu, and Run-Chu Zhang. "Choice of optimal initial designs in sequential experiments." Metrika 61, no. 2 (April 2005): 127–35. http://dx.doi.org/10.1007/s001840400327.

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43

Lee, Jung Hwan, and Myung Won Suh. "Development of Optimization Algorithm Using Sequential Design of Experiments and Micro-Genetic Algorithm." Transactions of the Korean Society of Mechanical Engineers A 38, no. 5 (May 1, 2014): 489–95. http://dx.doi.org/10.3795/ksme-a.2014.38.5.489.

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44

Rafaila, M., Ch Grimm, Ch Decker, and G. Pelz. "Sequential design of experiments for effective model-based validation of electronic control units." e & i Elektrotechnik und Informationstechnik 127, no. 6 (June 2010): 164–70. http://dx.doi.org/10.1007/s00502-010-0738-x.

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45

Crombecq, Karel, Dirk Gorissen, Dirk Deschrijver, and Tom Dhaene. "A Novel Hybrid Sequential Design Strategy for Global Surrogate Modeling of Computer Experiments." SIAM Journal on Scientific Computing 33, no. 4 (January 2011): 1948–74. http://dx.doi.org/10.1137/090761811.

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46

Azadi, Nammam Ali, Paul Fearnhead, Gareth Ridall, and Joleen H. Blok. "Bayesian Sequential Experimental Design for Binary Response Data with Application to Electromyographic Experiments." Bayesian Analysis 9, no. 2 (June 2014): 287–306. http://dx.doi.org/10.1214/13-ba855.

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47

Gilmour, S. G., and R. Mead. "Fixing a factor in the sequential design of two-level fractional factorial experiments." Journal of Applied Statistics 23, no. 1 (February 1996): 21–30. http://dx.doi.org/10.1080/02664769624323.

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48

Mu, Rongji, Lixiang Dai, and Jin Xu. "Sequential design for response surface model fit in computer experiments using derivative information." Communications in Statistics - Simulation and Computation 46, no. 2 (November 2, 2016): 1148–55. http://dx.doi.org/10.1080/03610918.2014.992543.

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49

Bect, Julien, David Ginsbourger, Ling Li, Victor Picheny, and Emmanuel Vazquez. "Sequential design of computer experiments for the estimation of a probability of failure." Statistics and Computing 22, no. 3 (April 21, 2011): 773–93. http://dx.doi.org/10.1007/s11222-011-9241-4.

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50

Imani, Mahdi, Roozbeh Dehghannasiri, Ulisses M. Braga-Neto, and Edward R. Dougherty. "Sequential Experimental Design for Optimal Structural Intervention in Gene Regulatory Networks Based on the Mean Objective Cost of Uncertainty." Cancer Informatics 17 (January 2018): 117693511879024. http://dx.doi.org/10.1177/1176935118790247.

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Scientists are attempting to use models of ever-increasing complexity, especially in medicine, where gene-based diseases such as cancer require better modeling of cell regulation. Complex models suffer from uncertainty and experiments are needed to reduce this uncertainty. Because experiments can be costly and time-consuming, it is desirable to determine experiments providing the most useful information. If a sequence of experiments is to be performed, experimental design is needed to determine the order. A classical approach is to maximally reduce the overall uncertainty in the model, meaning maximal entropy reduction. A recently proposed method takes into account both model uncertainty and the translational objective, for instance, optimal structural intervention in gene regulatory networks, where the aim is to alter the regulatory logic to maximally reduce the long-run likelihood of being in a cancerous state. The mean objective cost of uncertainty (MOCU) quantifies uncertainty based on the degree to which model uncertainty affects the objective. Experimental design involves choosing the experiment that yields the greatest reduction in MOCU. This article introduces finite-horizon dynamic programming for MOCU-based sequential experimental design and compares it with the greedy approach, which selects one experiment at a time without consideration of the full horizon of experiments. A salient aspect of the article is that it demonstrates the advantage of MOCU-based design over the widely used entropy-based design for both greedy and dynamic programming strategies and investigates the effect of model conditions on the comparative performances.

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