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Relevant bibliographies by topics / Experiments designs

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Academic literature on the topic 'Experiments designs'

Author: Grafiati

Published: 31 August 2024

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Journal articles on the topic "Experiments designs"

1

Fearn, Tom. "Design of Experiments 2: Factorial Designs." NIR news 18, no. 3 (May 2007): 14–15. http://dx.doi.org/10.1255/nirn.1020.

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Fearn, Tom. "Design of Experiments 3: 2k Factorial Designs." NIR news 18, no. 4 (June 2007): 18. http://dx.doi.org/10.1255/nirn.1026.

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Fearn, Tom. "Design of Experiments 4: Fractional Factorial Designs." NIR news 18, no. 5 (August 2007): 14–15. http://dx.doi.org/10.1255/nirn.1035.

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Fearn, Tom. "Design of Experiments 5: Response Surface Designs." NIR news 18, no. 7 (November 2007): 14–15. http://dx.doi.org/10.1255/nirn.1048.

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Sacks, Jerome, Susannah B. Schiller, and William J. Welch. "Designs for Computer Experiments." Technometrics 31, no. 1 (February 1989): 41–47. http://dx.doi.org/10.1080/00401706.1989.10488474.

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Kanninen, Barbara J. "Optimal Design for Multinomial Choice Experiments." Journal of Marketing Research 39, no. 2 (May 2002): 214–27. http://dx.doi.org/10.1509/jmkr.39.2.214.19080.

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The author derives D-optimal designs for main-effects, multinomial choice experiments using attribute levels as design parameters. The design solutions are similar to standard main-effects designs except that one attribute is used to manipulate response probabilities. The manipulator is key to implementing optimal designs in practice.

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Edmondson, Rodney N. "Multi-level Block Designs for Comparative Experiments." Journal of Agricultural, Biological and Environmental Statistics 25, no. 4 (October 8, 2020): 500–522. http://dx.doi.org/10.1007/s13253-020-00416-0.

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Abstract Complete replicate block designs are fully efficient for treatment effects and are the designs of choice for many agricultural field experiments. For experiments with a large number of treatments, however, they may not provide good control of variability over the whole experimental area. Nested incomplete block designs with a single level of nesting can then improve ‘within-block’ homogeneity for moderate sized experiments. For very large designs, however, a single level of nesting may not be adequate and this paper discusses multi-level nesting with hierarchies of nested blocks. Multi-level nested block designs provide a range of block sizes which can improve ‘within-block’ homogeneity over a range of scales of measurement. We discuss design and analysis of multi-level block designs for hierarchies of nested blocks including designs with crossed block factors. We describe an R language package for multi-level block design and we exemplify the design and analysis of multi-level block designs by a simulation study of block designs for cereal variety trials in the UK. Finally, we re-analyse a single large row-and-column field trial for 272 spring barley varieties in 16 rows and 34 columns assuming an additional set of multi-level nested column blocks superimposed on the existing design. For each example, a multi-level mixed blocks analysis is compared with a spatial analysis based on hierarchical generalized additive (HGAM) models. We discuss the combined analysis of random blocks and HGAM smoothers in the same model.

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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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Morris, Max D., and Toby J. Mitchell. "Exploratory designs for computational experiments." Journal of Statistical Planning and Inference 43, no. 3 (February 1995): 381–402. http://dx.doi.org/10.1016/0378-3758(94)00035-t.

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Georgiou, S. D. "Orthogonal designs for computer experiments." Journal of Statistical Planning and Inference 141, no. 4 (April 2011): 1519–25. http://dx.doi.org/10.1016/j.jspi.2010.11.014.

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Dissertations / Theses on the topic "Experiments designs"

1

周敬良 and 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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Books on the topic "Experiments designs"

1

M, Cox Gertrude, ed. Experimental designs. 2nd ed. New York: Wiley, 1992.

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Dey, Aloke. Orthogonal fractional factorial designs. Chichester: Wiley Eastern, 1985.

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Yürekli, Hülya Uluçay, and Esin Yürekli. Deneyler: Architecture. Experiments. İstanbul: Yapı-Endüstri Merkezi Yayınları, 2008.

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4

1944-, Dodge Yadolah, Fedorov V. V, Wynn Henry P, and International Conference-Workshop on Optimal Design and Analysis of Experiments (1st : 1988 : Neuchâtel, Switzerland), eds. Optimal design and analysis of experiments. Amsterdam: North-Holland, 1988.

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Grove, Daniel M. Engineering, quality, and experimental design. Harlow, Essex, England: Longman Scientific & Technical, 1992.

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Prancan, Kathi, ed. Experimental and Quasi-Experimental Designs: For Generalized Causal Inference. Boston, USA: Houghton Mifflin Company, 2001.

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E. E. M. van Berkum. Optimal paired comparison designs for factorial experiments. Amsterdam: Matematisch Centrum, 1987.

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Pepin, Gerard R. Traffic model for advanced satellite designs and experiments. Washington, DC: National Aeronautics and Space Administration, 1991.

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Durivage, Mark Allen. Practical design of experiments (DOE): A guide for optimizing designs and processes. Milwaukee, Wisconsin: ASQ Quality Press, 2016.

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Gunst, Richard F. How to construct fractional factorial experiments. Milwaukee, Wis: ASQC Quality Press, 1991.

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Book chapters on the topic "Experiments designs"

1

Federer, Walter T. "Experiment Designs for Intercropping Experiments." In Springer Series in Statistics, 242–93. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4613-9305-4_10.

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John, J. A. "Factorial experiments: multiple replication." In Cyclic Designs, 151–81. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4899-3326-3_7.

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Heiberger, Richard M., and Burt Holland. "Design of Experiments—Factorial Designs." In Statistical Analysis and Data Display, 427–78. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2122-5_13.

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Heiberger, Richard M., and Burt Holland. "Design of Experiments—Complex Designs." In Statistical Analysis and Data Display, 479–538. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2122-5_14.

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Heiberger, Richard M., and Burt Holland. "Design of Experiments—Factorial Designs." In Statistical Analysis and Data Display, 381–427. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4757-4284-8_13.

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Heiberger, Richard M., and Burt Holland. "Design of Experiments—Complex Designs." In Statistical Analysis and Data Display, 429–85. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4757-4284-8_14.

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Sinha, B. K., N. K. Mandal, Manisha Pal, and P. Das. "Optimal Regression Designs." In Optimal Mixture Experiments, 9–21. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1786-2_2.

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Mariel, Petr, David Hoyos, Jürgen Meyerhoff, Mikolaj Czajkowski, Thijs Dekker, Klaus Glenk, Jette Bredahl Jacobsen, et al. "Experimental Design." In Environmental Valuation with Discrete Choice Experiments, 37–49. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62669-3_3.

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AbstractThis chapter covers various issues related to the experimental design, a statistical technique at the core of a discrete choice experiment. Specifically, it focuses on the dimensionality of a choice experiment and the statistical techniques used to allocate attribute levels to choice tasks. Among others, the pros and cons of orthogonal designs, optimal orthogonal in the differences designs as well as efficient designs are addressed. The last section shows how a simulation exercise can help to test the appropriateness of the experimental design.

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Coolican, Hugh. "Experiments and experimental designs in psychology." In Research Methods and Statistics in Psychology, 69–99. 8th ed. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003215967-4.

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John, J. A. "Factorial experiments: single and fractional replication." In Cyclic Designs, 116–50. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4899-3326-3_6.

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Conference papers on the topic "Experiments designs"

1

Han-Yu Chuang, Huai-Kuang Tsai, and Cheng-Yan Kao. "Optimal designs for microarray experiments." In 7th International Symposium on Parallel Architectures, Algorithms and Networks, 2004. Proceedings. IEEE, 2004. http://dx.doi.org/10.1109/ispan.2004.1300547.

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Johnson, Rachel T., Bradley Jones, John W. Fowler, and Douglas C. Montgomery. "Comparing designs for computer simulation experiments." In 2008 Winter Simulation Conference (WSC). IEEE, 2008. http://dx.doi.org/10.1109/wsc.2008.4736101.

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Feng, Mingbin, and Jeremy Staum. "Green simulation designs for repeated experiments." In 2015 Winter Simulation Conference (WSC). IEEE, 2015. http://dx.doi.org/10.1109/wsc.2015.7408182.

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Tew, Jeffrey D. "Using central composite designs in simulation experiments." In the 24th conference. New York, New York, USA: ACM Press, 1992. http://dx.doi.org/10.1145/167293.167634.

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Nazer, B., and R. D. Nowak. "Efficient designs for multiple gene knockdown experiments." In 2010 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2010. http://dx.doi.org/10.1109/gensips.2010.5719680.

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Amini, Roja Ezzati, Eva Michelaraki, Christos Katrakazas, Christelle Al Haddad, Bart De Vos, Ariane Cuenen, George Yannis, Tom Brijs, and Constantinos Antoniou. "Risk scenario designs for driving simulator experiments." In 2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS). IEEE, 2021. http://dx.doi.org/10.1109/mt-its49943.2021.9529268.

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VOIGT, TIM, MARVIN SCHONE, MARTIN KOHLHASE, OLIVER NELLES, and MARTIN KUHN. "Space-Filling Designs for Experiments with Assembled Products." In MSIE 2021: 2021 3rd International Conference on Management Science and Industrial Engineering. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3460824.3460854.

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Krawczyk, Rafał, Paweł Linczuk, Tomasz Czarski, Andrzej Wojeński, Maryna Chernyshova, Krzysztof Poźniak, Piotr Kolasiński, et al. "The computation in diagnostics for tokamaks: systems, designs, approaches." In Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2017, edited by Ryszard S. Romaniuk and Maciej Linczuk. SPIE, 2017. http://dx.doi.org/10.1117/12.2281004.

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Zabołotny, Wojciech M. "Version control friendly project management system for FPGA designs." In Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016, edited by Ryszard S. Romaniuk. SPIE, 2016. http://dx.doi.org/10.1117/12.2247944.

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Ziolkowski, Richard W. "Metamaterial-Inspired Efficient Electrically-Small Antennas: Designs and Experiments." In 2007 International Workshop on Anti-Counterfeiting, Security and Identification. IEEE, 2007. http://dx.doi.org/10.1109/iwasid.2007.373682.

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Reports on the topic "Experiments designs"

1

Morris, M. X., and T. J. Mitchell. Exploratory designs for computational experiments. Office of Scientific and Technical Information (OSTI), October 1992. http://dx.doi.org/10.2172/7192422.

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Morris, M. X., and T. J. Mitchell. Exploratory designs for computational experiments. Office of Scientific and Technical Information (OSTI), October 1992. http://dx.doi.org/10.2172/10184343.

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Williges, Robert C., and Sam E. Middlebrooks. An Automated Tool for Developing Experimental Designs: The Computer-Aided Design Reference for Experiments (CADRE). Fort Belvoir, VA: Defense Technical Information Center, January 2009. http://dx.doi.org/10.21236/ada493809.

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Green, Donald. Field Experiments: Design, Analysis, and Interpretation. Instats Inc., 2024. http://dx.doi.org/10.61700/sgeirzx0v0mjh1252.

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This five-day workshop, led by Donald Green from Columbia University, provides a comprehensive guide to designing, executing, and interpreting field experiments across various social science fields. Participants will receive a broad-based introduction to causal inference and experimental designs that address complications such as noncompliance or attrition, while gaining hands-on experience with statistical software and learning best practices for conducting scientifically rigorous and ethically sound research.

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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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Muralidharan, Karthik, Mauricio Romero, and Kaspar Wüthrich. Factorial Designs, Model Selection, and (Incorrect) Inference in Randomized Experiments. Cambridge, MA: National Bureau of Economic Research, December 2019. http://dx.doi.org/10.3386/w26562.

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Youker, Amanda J., Dominique C. Stepinski, and George F. Vandegrift. Direct Down-scale Experiments of Concentration Column Designs for SHINE Process. Office of Scientific and Technical Information (OSTI), May 2017. http://dx.doi.org/10.2172/1372108.

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Ghosh, Subir, and Xiao D. Zhang. Two New Series of Search Designs for 3(m) Factorial Experiments. Fort Belvoir, VA: Defense Technical Information Center, July 1986. http://dx.doi.org/10.21236/ada174429.

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Liu, Pu. Reporting and analysis of split plot designs in preclinical animal experiments. Ames (Iowa): Iowa State University, January 2020. http://dx.doi.org/10.31274/cc-20240624-1221.

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Jones, A. G., D. Snyder, and J. Spratt. Magnetotelluric and teleseismic experiments as part of the Walmsley Lake project, Northwest Territories: experimental designs and preliminary results. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2001. http://dx.doi.org/10.4095/212041.

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