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Journal articles on the topic 'Experiments designs'

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Published: 31 August 2024

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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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Piepel, Gregory F., and John A. Cornell. "Designs for Mixture-Amount Experiments." Journal of Quality Technology 19, no. 1 (January 1987): 11–28. http://dx.doi.org/10.1080/00224065.1987.11979029.

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12

Burgess, Leonie, and Deborah J. Street. "Optimal Designs for 2kChoice Experiments." Communications in Statistics - Theory and Methods 32, no. 11 (January 9, 2003): 2185–206. http://dx.doi.org/10.1081/sta-120024475.

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13

Kessels, Roselinde, Peter Goos, and Martina Vandebroek. "Optimal designs for conjoint experiments." Computational Statistics & Data Analysis 52, no. 5 (January 2008): 2369–87. http://dx.doi.org/10.1016/j.csda.2007.10.016.

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14

Mukkula, Anwesh Reddy Gottu, and Radoslav Paulen. "Robust design of optimal experiments considering consecutive re-designs." IFAC-PapersOnLine 55, no. 7 (2022): 13–18. http://dx.doi.org/10.1016/j.ifacol.2022.07.415.

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15

Phadke, Abhishek, F. Antonio Medrano, Chandra N. Sekharan, and Tianxing Chu. "Designing UAV Swarm Experiments: A Simulator Selection and Experiment Design Process." Sensors 23, no. 17 (August 23, 2023): 7359. http://dx.doi.org/10.3390/s23177359.

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The rapid advancement and increasing number of applications of Unmanned Aerial Vehicle (UAV) swarm systems have garnered significant attention in recent years. These systems offer a multitude of uses and demonstrate great potential in diverse fields, ranging from surveillance and reconnaissance to search and rescue operations. However, the deployment of UAV swarms in dynamic environments necessitates the development of robust experimental designs to ensure their reliability and effectiveness. This study describes the crucial requirement for comprehensive experimental design of UAV swarm systems before their deployment in real-world scenarios. To achieve this, we begin with a concise review of existing simulation platforms, assessing their suitability for various specific needs. Through this evaluation, we identify the most appropriate tools to facilitate one’s research objectives. Subsequently, we present an experimental design process tailored for validating the resilience and performance of UAV swarm systems for accomplishing the desired objectives. Furthermore, we explore strategies to simulate various scenarios and challenges that the swarm may encounter in dynamic environments, ensuring comprehensive testing and analysis. Complex multimodal experiments may require system designs that may not be completely satisfied by a single simulation platform; thus, interoperability between simulation platforms is also examined. Overall, this paper serves as a comprehensive guide for designing swarm experiments, enabling the advancement and optimization of UAV swarm systems through validation in simulated controlled environments.
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16

KUMAR, PRAKASH, KRISHAN LAL, ANIRBAN MUKHERJEE, UPENDRA KUMAR PRADHAN, MRINMOY RAY, and OM PRAKASH. "Advanced row-column designs for animal feed experiments." Indian Journal of Animal Sciences 88, no. 4 (January 5, 2023): 499–503. http://dx.doi.org/10.56093/ijans.v88i4.78895.

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Inappropriate statistical designs may misinterpret results of animal feed experiments. Thus complete statistical designs can make animal feed research more appropriate and cost effective. Usually factorial row-column designs are used when the heterogeneity in the experimental material is in two directions and the experimenter is interested in studying the effect of two or more factors simultaneously. Attempts have been to develop the method of construction of balanced nested row column design under factorial setup. Factorial experiments are used in designs when two or more factors have same levels or different levels. The designs that are balanced symmetric factorials nested in blocks are called block designs with nested row-column balanced symmetric factorial experiments. These designs were constructed by using confounding through equation methods.Construction of confounded asymmetrical factorial experiments in row-column settings and efficiency factor of confounded effects was worked out. The design can be used in animal feed experiment with fewer resources by not compromising the test accuracy.
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17

Lal, Kishan, Rajender Prasad, and V. K. Gupta. "Trend‐Free Nested Balanced Incomplete Block Designs and Designs for Diallel Cross Experiments." Calcutta Statistical Association Bulletin 59, no. 3-4 (September 2007): 203–21. http://dx.doi.org/10.1177/0008068320070306.

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Abstract: Nested balanced incomplete block (NBIB) designs are useful when the experiments are conducted to deal with experimental situations when one nuisance factor is nested within the blocking factor. Similar to block designs, trend may exist in experimental units within sub‐blocks or within blocks in NBIB designs over time or space. A necessary and sufficient condition, for a nested block design to be trend‐free at sub‐block level, is derived. Families and catalogues of NBIB designs that can be converted into trend‐free NBIB designs at sub‐block and block levels have been obtained. A NBIB design with sub‐block size 2 has a one to one correspondence with designs for diallel crosses experiments. Therefore, optimal block designs for dialled cross experiments have been identified to check if these can be converted in to trend‐free optimal block designs for diallel cross experiments. A catalogue of such designs is also obtained. Trend‐free design is illustrated with example for a NBIB design and a design for diallel crosses experiments. AMS (2000) Subject Classification: 62K05, 62K10.
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18

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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19

Bailey, R. A. "Designs for two-colour microarray experiments." Journal of the Royal Statistical Society: Series C (Applied Statistics) 56, no. 4 (August 2007): 365–94. http://dx.doi.org/10.1111/j.1467-9876.2007.00582.x.

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20

Ahn, Hongshik, Wei Zhu, Joonsook Yang, and Ralph L. Kodell. "Efficient designs for animal carcinogenicity experiments." Communications in Statistics - Theory and Methods 27, no. 6 (January 1998): 1275–87. http://dx.doi.org/10.1080/03610929808832158.

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21

Ba, Shan, and V. Roshan Joseph. "Multi-Layer Designs for Computer Experiments." Journal of the American Statistical Association 106, no. 495 (September 2011): 1139–49. http://dx.doi.org/10.1198/jasa.2011.tm10229.

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22

Joseph, V. Roshan, Li Gu, Shan Ba, and William R. Myers. "Space-Filling Designs for Robustness Experiments." Technometrics 61, no. 1 (June 18, 2018): 24–37. http://dx.doi.org/10.1080/00401706.2018.1451390.

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23

Altan, Stan, and Jyh-Ming Shoung. "Block Designs in Method Transfer Experiments." Journal of Biopharmaceutical Statistics 18, no. 5 (September 5, 2008): 996–1004. http://dx.doi.org/10.1080/10543400802287339.

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24

Stylianou, S. "Foldover Conference Designs for Screening Experiments." Communications in Statistics - Theory and Methods 39, no. 10 (May 12, 2010): 1776–84. http://dx.doi.org/10.1080/03610920902898506.

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25

Mann, Rena K., Roderick Edwards, and Julie Zhou. "Robust designs for experiments with blocks." Communications in Statistics - Theory and Methods 45, no. 18 (December 21, 2015): 5363–79. http://dx.doi.org/10.1080/03610926.2014.942434.

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26

Prescott, Philip. "Nearly Uniform Designs for Mixture Experiments." Communications in Statistics - Theory and Methods 37, no. 13 (May 9, 2008): 2095–115. http://dx.doi.org/10.1080/03610920701824257.

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27

Gupta, V. K., Rajender Parsad, Lal Mohan Bhar, and Basudev Kole. "Supersaturated Designs for Asymmetrical Factorial Experiments." Journal of Statistical Theory and Practice 2, no. 1 (March 2008): 95–108. http://dx.doi.org/10.1080/15598608.2008.10411863.

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28

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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29

Stinstra, Erwin, Dick den Hertog, Peter Stehouwer, and Arjen Vestjens. "Constrained Maximin Designs for Computer Experiments." Technometrics 45, no. 4 (November 2003): 340–46. http://dx.doi.org/10.1198/004017003000000168.

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30

Chow, K. L. "A-Optimal Designs for Supplementary Experiments." Theory of Probability & Its Applications 37, no. 1 (January 1993): 126–29. http://dx.doi.org/10.1137/1137027.

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31

Asprey, S. P., S. Macchietto, and C. C. Pantelides. "Robust Optimal Designs for Dynamic Experiments." IFAC Proceedings Volumes 33, no. 10 (June 2000): 845–50. http://dx.doi.org/10.1016/s1474-6670(17)38645-7.

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32

R�der, Ingo, and Holger Dette. "Optimal discrimination designs for multifactor experiments." Annals of Statistics 25, no. 3 (June 1997): 1161–75. http://dx.doi.org/10.1214/aos/1069362742.

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33

John, J. A., and Katya Ruggiero. "Resolvable block designs for factorial experiments." Journal of Statistical Planning and Inference 77, no. 2 (March 1999): 293–99. http://dx.doi.org/10.1016/s0378-3758(98)00185-2.

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34

Das, Ashish, Aloke Dey, and Angela M. Dean. "Optimal designs for diallel cross experiments." Statistics & Probability Letters 36, no. 4 (January 1998): 427–36. http://dx.doi.org/10.1016/s0167-7152(97)00090-4.

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35

Ohnishi, Toshio, and Teruhiro Shirakura. "Search designs for 2m factorial experiments." Journal of Statistical Planning and Inference 11, no. 2 (February 1985): 241–45. http://dx.doi.org/10.1016/0378-3758(85)90011-4.

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36

Hilgers, Ralf-Dieter, and Peter Bauer. "Optimal designs for mixture amount experiments." Journal of Statistical Planning and Inference 48, no. 2 (November 1995): 241–46. http://dx.doi.org/10.1016/0378-3758(95)00003-r.

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37

Joseph, V. Roshan, Evren Gul, and Shan Ba. "Maximum projection designs for computer experiments." Biometrika 102, no. 2 (March 18, 2015): 371–80. http://dx.doi.org/10.1093/biomet/asv002.

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38

Bursztyn, Dizza, and David M. Steinberg. "Comparison of designs for computer experiments." Journal of Statistical Planning and Inference 136, no. 3 (March 2006): 1103–19. http://dx.doi.org/10.1016/j.jspi.2004.08.007.

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39

Pepelyshev, Andrey, Irene Poli, and Viatcheslav Melas. "Uniform coverage designs for mixture experiments." Journal of Statistical Planning and Inference 139, no. 10 (October 2009): 3442–52. http://dx.doi.org/10.1016/j.jspi.2009.03.020.

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40

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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41

Ou, Beiyan, and Julie Zhou. "Minimax robust designs for field experiments." Metrika 69, no. 1 (February 19, 2008): 45–54. http://dx.doi.org/10.1007/s00184-008-0173-8.

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42

Lewis, S. M., and M. G. Tuck. "Paired Comparison Designs for Factorial Experiments." Applied Statistics 34, no. 3 (1985): 227. http://dx.doi.org/10.2307/2347467.

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43

Street, Deborah J., David S. Bunch, and Beverley J. Moore. "OPTIMAL DESIGNS FOR 2kPAIRED COMPARISON EXPERIMENTS." Communications in Statistics - Theory and Methods 30, no. 10 (August 31, 2001): 2149–71. http://dx.doi.org/10.1081/sta-100106068.

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44

Dean, A. M., and S. M. Lewis. "Multidimensional Designs for Two-Factor Experiments." Journal of the American Statistical Association 87, no. 420 (December 1992): 1158–65. http://dx.doi.org/10.1080/01621459.1992.10476273.

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45

Kong, Xiangshun, Mingyao Ai, and Kwok Leung Tsui. "Flexible sliced designs for computer experiments." Annals of the Institute of Statistical Mathematics 70, no. 3 (February 25, 2017): 631–46. http://dx.doi.org/10.1007/s10463-017-0603-3.

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46

Matthews, J. N. S., and A. B. Forbes. "Stepped wedge designs: insights from a design of experiments perspective." Statistics in Medicine 36, no. 24 (August 8, 2017): 3772–90. http://dx.doi.org/10.1002/sim.7403.

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47

CLIFFORD, SCOTT, GEOFFREY SHEAGLEY, and SPENCER PISTON. "Increasing Precision without Altering Treatment Effects: Repeated Measures Designs in Survey Experiments." American Political Science Review 115, no. 3 (April 12, 2021): 1048–65. http://dx.doi.org/10.1017/s0003055421000241.

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The use of survey experiments has surged in political science. The most common design is the between-subjects design in which the outcome is only measured posttreatment. This design relies heavily on recruiting a large number of subjects to precisely estimate treatment effects. Alternative designs that involve repeated measurements of the dependent variable promise greater precision, but they are rarely used out of fears that these designs will yield different results than a standard design (e.g., due to consistency pressures). Across six studies, we assess this conventional wisdom by testing experimental designs against each other. Contrary to common fears, repeated measures designs tend to yield the same results as more common designs while substantially increasing precision. These designs also offer new insights into treatment effect size and heterogeneity. We conclude by encouraging researchers to adopt repeated measures designs and providing guidelines for when and how to use them.
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48

Mu, Weiyan, Chengxin Liu, and Shifeng Xiong. "Nested Maximum Entropy Designs for Computer Experiments." Mathematics 11, no. 16 (August 18, 2023): 3572. http://dx.doi.org/10.3390/math11163572.

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Presently, computer experiments with multiple levels of accuracy are widely applied in science and engineering. This paper introduces a class of nested maximum entropy designs for such computer experiments. A multi-layer DETMAX algorithm is proposed to construct nested maximum entropy designs. Based on nested maximum entropy designs, we also propose an integer-programming procedure to specify the sample sizes in multi-fidelity computer experiments. Simulated annealing techniques are used to tackle complex optimization problems in the proposed methods. Illustrative examples show that the proposed nested entropy designs can yield better prediction results than nested Latin hypercube designs in the literature and that the proposed sample-size determination method is effective.
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49

João Gilberto Corrêa da Silva. "Principles of the experiment design." World Journal of Advanced Research and Reviews 22, no. 1 (April 30, 2024): 470–86. http://dx.doi.org/10.30574/wjarr.2024.22.1.1094.

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Reference books usually present experiment designs as recipes, one of which should be chosen for each experiment. This approach extends to teaching and leads the researcher to understand that he is limited to the use of these experiment designs. The consequences are the adaptation of research plans to this restrict set of designs and the adoption of inappropriate designs to achieve research objectives. This approach arose from the relatively simple calculations required to analyze the results of experiments with those designs at a time when computing resources were precarious. The evolution of computing resources no longer justifies the restriction to experiment designs that require easy calculations. These resources made possible the elaboration of designs with properties appropriate for efficient experiments. This article considers the properties that constitute principles of the experiment design that must be considered when planning the experiment. Compliance with these principles allows the researcher to elaborate the most appropriate experiment design for each experiment.
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50

Hoefler, Raegan, Pablo González-Barrios, Madhav Bhatta, Jose A. R. Nunes, Ines Berro, Rafael S. Nalin, Alejandra Borges, et al. "Do Spatial Designs Outperform Classic Experimental Designs?" Journal of Agricultural, Biological and Environmental Statistics 25, no. 4 (August 29, 2020): 523–52. http://dx.doi.org/10.1007/s13253-020-00406-2.

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Abstract Controlling spatial variation in agricultural field trials is the most important step to compare treatments efficiently and accurately. Spatial variability can be controlled at the experimental design level with the assignment of treatments to experimental units and at the modeling level with the use of spatial corrections and other modeling strategies. The goal of this study was to compare the efficiency of methods used to control spatial variation in a wide range of scenarios using a simulation approach based on real wheat data. Specifically, classic and spatial experimental designs with and without a two-dimensional autoregressive spatial correction were evaluated in scenarios that include differing experimental unit sizes, experiment sizes, relationships among genotypes, genotype by environment interaction levels, and trait heritabilities. Fully replicated designs outperformed partially and unreplicated designs in terms of accuracy; the alpha-lattice incomplete block design was best in all scenarios of the medium-sized experiments. However, in terms of response to selection, partially replicated experiments that evaluate large population sizes were superior in most scenarios. The AR1 $$\times $$ × AR1 spatial correction had little benefit in most scenarios except for the medium-sized experiments with the largest experimental unit size and low GE. Overall, the results from this study provide a guide to researchers designing and analyzing large field experiments. Supplementary materials accompanying this paper appear online.
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