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Journal articles on the topic 'Multiple robustness'

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Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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1

Coko, Klodian, and Jutta Schickore. "Robustness, solidity, and multiple determinations." Metascience 22, no. 3 (February 6, 2013): 681–83. http://dx.doi.org/10.1007/s11016-013-9750-1.

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2

Molina, J., A. Rotnitzky, M. Sued, and J. M. Robins. "Multiple robustness in factorized likelihood models." Biometrika 104, no. 3 (June 15, 2017): 561–81. http://dx.doi.org/10.1093/biomet/asx027.

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3

Wang, Lei. "Multiple robustness estimation in causal inference." Communications in Statistics - Theory and Methods 48, no. 23 (December 4, 2018): 5701–18. http://dx.doi.org/10.1080/03610926.2018.1520881.

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4

Ratliff, Jacob, Alessio Franci, Eve Marder, and Timothy O’Leary. "Neuronal oscillator robustness to multiple global perturbations." Biophysical Journal 120, no. 8 (April 2021): 1454–68. http://dx.doi.org/10.1016/j.bpj.2021.01.038.

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5

Holland, Burt, and Siu Hung Cheung. "Familywise robustness criteria for multiple-comparison procedures." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64, no. 1 (January 2002): 63–77. http://dx.doi.org/10.1111/1467-9868.00325.

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6

Cazzavillan, Guido, and Patrick A. Pintus. "Robustness of multiple equilibria in OLG economies." Review of Economic Dynamics 7, no. 2 (April 2004): 456–75. http://dx.doi.org/10.1016/j.red.2003.10.001.

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7

Clarke, Sandy, and Peter Hall. "Robustness of multiple testing procedures against dependence." Annals of Statistics 37, no. 1 (February 2009): 332–58. http://dx.doi.org/10.1214/07-aos557.

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8

Dong, Gaogao, Yan Chen, Fan Wang, Ruijin Du, Lixin Tian, and H. Eugene Stanley. "Robustness on interdependent networks with a multiple-to-multiple dependent relationship." Chaos: An Interdisciplinary Journal of Nonlinear Science 29, no. 7 (July 2019): 073107. http://dx.doi.org/10.1063/1.5093074.

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9

Kwon, Yung-Keun, Junil Kim, and Kwang-Hyun Cho. "Dynamical Robustness against Multiple Mutations in Signaling Networks." IEEE/ACM Transactions on Computational Biology and Bioinformatics 13, no. 5 (September 1, 2016): 996–1002. http://dx.doi.org/10.1109/tcbb.2015.2495251.

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10

Rangavajhala, Sirisha, and Sankaran Mahadevan. "Design optimization for robustness in multiple performance functions." Structural and Multidisciplinary Optimization 47, no. 4 (December 13, 2012): 523–38. http://dx.doi.org/10.1007/s00158-012-0860-y.

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11

Liu, Zhi, Xian-Fu Zhang, and Yu-Zhen Wang. "Robustness analysis for switched multiple equilibrium points systems." International Journal of Control, Automation and Systems 15, no. 6 (December 2017): 2628–35. http://dx.doi.org/10.1007/s12555-016-0367-1.

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12

Rudolph, P. E. "Robustness of Multiple Comparison Procedures: Treatment versus Control." Biometrical Journal 30, no. 1 (1988): 41–45. http://dx.doi.org/10.1002/bimj.4710300106.

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13

Wierzbicki, Andrzej P., Marek Makowski, and Janusz Granat. "Robustness Testing of Model Based Multiple Criteria Decisions: Fundamentals and Applications." International Journal of Information Technology & Decision Making 14, no. 05 (September 2015): 1035–62. http://dx.doi.org/10.1142/s0219622015500157.

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Robustness or insensitivity is a desirable property of decisions; however, most texts on robustness and/or sensitivity analysis do not define it precisely. A broad literature in this field concentrates on robust design of decisions (including robust optimization). This paper focuses on robustness testing, that is, checking whether a design has actually resulted in robust properties of the system if some of basic assumptions are changed. We propose a general framework of such robustness testing and show that robustness is a property of the relation between three (classes of) models: a basic model of the decision situation, a second model of possible perturbations of the first model, and a model of implementation of the decision, optionally taking into account some measurements of the impact of perturbations. Typical approaches to robustness or sensitivity analysis assume tacitly that the first two models can be combined and analyze parameters deviations in one combined model. However, the role of the first two models can be asymmetric if some optimization of the decision is performed on the first model. We extend this framework, intended originally for single criteria (scalar) optimization to multiple criteria (vector) optimization. The proposed approach is illustrated by diverse examples.
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14

Zhou, Peng, Liang Du, Yi-Dong Shen, and Xuejun Li. "Tri-level Robust Clustering Ensemble with Multiple Graph Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 11125–33. http://dx.doi.org/10.1609/aaai.v35i12.17327.

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Clustering ensemble generates a consensus clustering result by integrating multiple weak base clustering results. Although it often provides more robust results compared with single clustering methods, it still suffers from the robustness problem if it does not treat the unreliability of base results carefully. Conventional clustering ensemble methods often use all data for ensemble, while ignoring the noises or outliers on the data. Although some robust clustering ensemble methods are proposed, which extract the noises on the data, they still characterize the robustness in a single level, and thus they cannot comprehensively handle the complicated robustness problem. In this paper, to address this problem, we propose a novel Tri-level Robust Clustering Ensemble (TRCE) method by transforming the clustering ensemble problem to a multiple graph learning problem. Just as its name implies, the proposed method tackles robustness problem in three levels: base clustering level, graph level and instance level. By considering the robustness problem in a more comprehensive way, the proposed TRCE can achieve a more robust consensus clustering result. Experimental results on benchmark datasets also demonstrate it. Our method often outperforms other state-of-the-art clustering ensemble methods. Even compared with the robust ensemble methods, ours also performs better.
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15

Zhang, Xin, Bo He, Shuang Gao, Pengcheng Mu, Junchao Xu, and Ning Zhai. "Multiple model AUV navigation methodology with adaptivity and robustness." Ocean Engineering 254 (June 2022): 111258. http://dx.doi.org/10.1016/j.oceaneng.2022.111258.

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16

Zhang, Xin, Bo He, Shuang Gao, Pengcheng Mu, Junchao Xu, and Ning Zhai. "Multiple model AUV navigation methodology with adaptivity and robustness." Ocean Engineering 254 (June 2022): 111258. http://dx.doi.org/10.1016/j.oceaneng.2022.111258.

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17

Seo, Jung-Hee, and Hung-Bog Park. "Multiple Barcode Watermarking Technique for Improve Robustness and Imperceptibility." Journal of the Korea Institute of Information and Communication Engineering 20, no. 9 (September 30, 2016): 1723–29. http://dx.doi.org/10.6109/jkiice.2016.20.9.1723.

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18

Gurevich, Lilia, Rivka Cohen-Luria, Nathaniel Wagner, and Gonen Ashkenasy. "Robustness of synthetic circadian clocks to multiple environmental changes." Chemical Communications 51, no. 26 (2015): 5672–75. http://dx.doi.org/10.1039/c5cc00098j.

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19

Satorra, Albert. "ASYMPTOTIC ROBUSTNESS IN MULTIPLE GROUP LINEAR-LATENT VARIABLE MODELS." Econometric Theory 18, no. 2 (April 2002): 297–312. http://dx.doi.org/10.1017/s0266466602182041.

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Standard methods for analyzing linear-latent variable models rely on the assumption that the observed variables are normally distributed. Normality allows statistical inferences to be carried out based solely on the first-and second-order moments. In general, inferences for nonnormally distributed data require the estimates of matrices of third-and fourth-order moments. In the present paper, we show that inferences based on normal theory retain validity and asymptotic efficiency under general assumptions that allow for considerable departure from normality. In particular, we obtain conditions under which correct asymptotic inferences are attained when replacing a matrix of higher order moments by a matrix that depends only on cross-product moments of the data.
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20

Gao, Yan-Li, Shi-Ming Chen, Sen Nie, Fei Ma, and Jun-Jie Guan. "Robustness analysis of interdependent networks under multiple-attacking strategies." Physica A: Statistical Mechanics and its Applications 496 (April 2018): 495–504. http://dx.doi.org/10.1016/j.physa.2017.12.085.

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21

Grujić, Lj T., and Dj Petkovski. "On robustness of Lurie systems with multiple non-linearities." Automatica 23, no. 3 (May 1987): 327–34. http://dx.doi.org/10.1016/0005-1098(87)90006-9.

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22

Daniel, Rhian M., and Michael G. Kenward. "A method for increasing the robustness of multiple imputation." Computational Statistics & Data Analysis 56, no. 6 (June 2012): 1624–43. http://dx.doi.org/10.1016/j.csda.2011.10.006.

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23

Kang, Ji-Won, Jae-Eun Lee, Jang-Hwan Choi, Woosuk Kim, Jin-Kyum Kim, Dong-Wook Kim, and Young-Ho Seo. "Digital Hologram Watermarking Based on Multiple Deep Neural Networks Training Reconstruction and Attack." Sensors 21, no. 15 (July 22, 2021): 4977. http://dx.doi.org/10.3390/s21154977.

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This paper proposes a method to embed and extract a watermark on a digital hologram using a deep neural network. The entire algorithm for watermarking digital holograms consists of three sub-networks. For the robustness of watermarking, an attack simulation is inserted inside the deep neural network. By including attack simulation and holographic reconstruction in the network, the deep neural network for watermarking can simultaneously train invisibility and robustness. We propose a network training method using hologram and reconstruction. After training the proposed network, we analyze the robustness of each attack and perform re-training according to this result to propose a method to improve the robustness. We quantitatively evaluate the results of robustness against various attacks and show the reliability of the proposed technique.
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24

Lawrence, Nicholas P., Brian W. H. Ng, Hedley J. Hansen, and Derek Abbott. "Analysis of millimetre-wave polarization diverse multiple-input multiple-output capacity." Royal Society Open Science 2, no. 12 (December 2015): 150322. http://dx.doi.org/10.1098/rsos.150322.

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Millimetre-waves offer the possibility of wide bandwidth and consequently high data rate for wireless communications. For both uni- and dual-polarized systems, signals sent over a link may suffer severe degradation due to antenna misalignment. Orientation robustness may be enhanced by the use of mutual orthogonality in three dimensions. Multiple-input multiple-output polarization diversity offers a way of improving signal reception without the limitations associated with spatial diversity. Scattering effects often assist propagation through multipath. However, high path loss at millimetre-wave frequencies may limit any reception enhancement through scattering. We show that the inclusion of a third orthogonal dipole provides orientation robustness in this setting, as well as in a rich scattering environment, by means of a Rician fading channel model covering all orientations for a millimetre-wave, tri-orthogonal, half-wave dipole transmitter and receiver employing polarization diversity. Our simulation extends the analysis into three dimensions, fully exploiting individual sub-channel paths. In both the presence and absence of multipath effects, capacity is observed to be higher than that of a dual-polarized system over the majority of a field of view.
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25

WANG, Hui, and Wei Cun Zhang. "Weighted Multiple Model Adaptive Control Based on ADRC." Applied Mechanics and Materials 313-314 (March 2013): 412–17. http://dx.doi.org/10.4028/www.scientific.net/amm.313-314.412.

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Weighted multiple model adaptive control is a combination of off-line design and on-line decision, which combines a finite number of simple controllers by weighting algorithm. Its an effective means to solve the control problems of complex and uncertain systems. Active Disturbance Rejection Control (ADRC) is a new digital control technology with high accuracy and strong robustness. This paper introduces ADRC as local controller in the weighted multiple model adaptive control system. The simulation results show that the proposed system has strong robustness in a wide range.
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26

Shih, Frank Y., and Xin Zhong. "Achieving Image Watermarking Robustness by Geometric Rectification." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 04 (February 2, 2017): 1754007. http://dx.doi.org/10.1142/s0218001417540076.

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Image watermarking techniques have been widely used for copyright protection, broadcast monitoring, and data authentication. In this paper, we present a novel watermarking scheme which allows automatic selection of multiple regions-of-interest (ROIs) with robustness against geometric distortion. The fidelity of watermarked images is ensured by preserving salient foreground objects. The proposed scheme achieves watermarking robustness by geometric rectification, which is based on matching feature points between the salient foreground objects of a host image and its distorted stego-image. Experimental results show that the proposed technique can successfully obtain high fidelity and high robustness on an image dataset of multiple salient foreground objects.
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27

N.Sujatha, C., and P. Satyanarayana. "Analysis of Robustness of Hybrid Video Watermarking Against Multiple Attacks." International Journal of Computer Applications 118, no. 22 (May 20, 2015): 12–19. http://dx.doi.org/10.5120/20876-3614.

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28

Wang, Shuliang, Sen Nie, Longfeng Zhao, and H. Eugene Stanley. "A multiple perspective method for urban subway network robustness analysis." AIP Advances 8, no. 7 (July 2018): 075219. http://dx.doi.org/10.1063/1.5023766.

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29

Zhang, Yi, Xiangyang Luo, Yanqing Guo, Chuan Qin, and Fenlin Liu. "Multiple Robustness Enhancements for Image Adaptive Steganography in Lossy Channels." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 8 (August 2020): 2750–64. http://dx.doi.org/10.1109/tcsvt.2019.2923980.

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30

Behre, Jörn, Thomas Wilhelm, Axel von Kamp, Eytan Ruppin, and Stefan Schuster. "Structural robustness of metabolic networks with respect to multiple knockouts." Journal of Theoretical Biology 252, no. 3 (June 2008): 433–41. http://dx.doi.org/10.1016/j.jtbi.2007.09.043.

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31

Dexter, Joseph P., Tathagata Dasgupta, and Jeremy Gunawardena. "Invariants reveal multiple forms of robustness in bifunctional enzyme systems." Integrative Biology 7, no. 8 (May 29, 2015): 883–94. http://dx.doi.org/10.1039/c5ib00009b.

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32

Hazon, Noam, and Gal A. Kaminka. "On redundancy, efficiency, and robustness in coverage for multiple robots." Robotics and Autonomous Systems 56, no. 12 (December 2008): 1102–14. http://dx.doi.org/10.1016/j.robot.2008.01.006.

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33

Vetschera, Rudolf, Ye Chen, Keith W. Hipel, and D. Marc Kilgour. "Robustness and information levels in case-based multiple criteria sorting." European Journal of Operational Research 202, no. 3 (May 2010): 841–52. http://dx.doi.org/10.1016/j.ejor.2009.06.026.

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34

Reynolds, J. E., S. Halldin, J. Seibert, C. Y. Xu, and T. Grabs. "Robustness of flood-model calibration using single and multiple events." Hydrological Sciences Journal 65, no. 5 (May 17, 2019): 842–53. http://dx.doi.org/10.1080/02626667.2019.1609682.

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35

Oosterlee, C. W., and P. Wesseling. "On the Robustness of a Multiple Semi-coarsened Grid Method." ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik 75, no. 4 (1995): 251–57. http://dx.doi.org/10.1002/zamm.19950750402.

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36

Fuller, Gareth. "Credentialed Fictions and Robustness Analysis." Southwest Philosophy Review 38, no. 1 (2022): 135–43. http://dx.doi.org/10.5840/swphilreview202238114.

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In this paper I defend the possibility of robustness analysis as confirmatory. Given that models are highly idealized, multiple models with different sets of idealizations are constructed to show that some result is not dependent on those idealizations (it is robust). This method of robustness analysis has been criticized since, no matter how many false models agree, all of them are false and lack confirmatory power. I argue that this line of criticism makes an assumption that a model is confirmatory only if it ontically represents its target. I draw on work about explanations to motivate a challenge to this assumption, and argue that this assumption needs bolstering.
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37

Takahashi, Sho, Masashi Hyodo, Takahiro Nishiyama, and Tatjana Pavlenko. "MULTIPLE COMPARISON PROCEDURES FOR HIGH-DIMENSIONAL DATA AND THEIR ROBUSTNESS UNDER NON-NORMALITY ." Journal of the Japanese Society of Computational Statistics 26, no. 1 (2013): 71–82. http://dx.doi.org/10.5183/jjscs.1211001_202.

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38

Wang, Jin Hua, De Qiang Fu, and Jie Cao. "An On-Line Adaptive Product Fusion Tracking Algorithm Based on Multiple-Cue." Applied Mechanics and Materials 303-306 (February 2013): 1536–42. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.1536.

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To solve the poor robustness problem that object tracking only by fixation fusion based on multiple clues, an online adaptive fusion tracking algorithm based on multiple clues is proposed. the weighting of the different clues is online adjusting based on scene change , the more the feature contributes, the more a larger value, the less the feature contributes ,the less a smaller value. Simulation results show that this algorithm provides better robustness and stability.
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39

Wang, Raymond, and Louis Kang. "Multiple bumps can enhance robustness to noise in continuous attractor networks." PLOS Computational Biology 18, no. 10 (October 10, 2022): e1010547. http://dx.doi.org/10.1371/journal.pcbi.1010547.

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A central function of continuous attractor networks is encoding coordinates and accurately updating their values through path integration. To do so, these networks produce localized bumps of activity that move coherently in response to velocity inputs. In the brain, continuous attractors are believed to underlie grid cells and head direction cells, which maintain periodic representations of position and orientation, respectively. These representations can be achieved with any number of activity bumps, and the consequences of having more or fewer bumps are unclear. We address this knowledge gap by constructing 1D ring attractor networks with different bump numbers and characterizing their responses to three types of noise: fluctuating inputs, spiking noise, and deviations in connectivity away from ideal attractor configurations. Across all three types, networks with more bumps experience less noise-driven deviations in bump motion. This translates to more robust encodings of linear coordinates, like position, assuming that each neuron represents a fixed length no matter the bump number. Alternatively, we consider encoding a circular coordinate, like orientation, such that the network distance between adjacent bumps always maps onto 360 degrees. Under this mapping, bump number does not significantly affect the amount of error in the coordinate readout. Our simulation results are intuitively explained and quantitatively matched by a unified theory for path integration and noise in multi-bump networks. Thus, to suppress the effects of biologically relevant noise, continuous attractor networks can employ more bumps when encoding linear coordinates; this advantage disappears when encoding circular coordinates. Our findings provide motivation for multiple bumps in the mammalian grid network.
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40

Tajima, Motoki, and Yudai Honma. "Robustness of Traffic Networks Focusing on Spatial Relationships between Multiple Routes." Journal of the City Planning Institute of Japan 53, no. 2 (October 25, 2018): 199–205. http://dx.doi.org/10.11361/journalcpij.53.199.

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41

Harada, Kensuke, and Makoto Kaneko. "Equilibrium Grasp and its Robustness for Multiple Objects under Gravitational Field." Journal of the Robotics Society of Japan 17, no. 1 (1999): 133–39. http://dx.doi.org/10.7210/jrsj.17.133.

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42

Deutscher, David, Isaac Meilijson, Martin Kupiec, and Eytan Ruppin. "Multiple knockout analysis of genetic robustness in the yeast metabolic network." Nature Genetics 38, no. 9 (August 29, 2006): 993–98. http://dx.doi.org/10.1038/ng1856.

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43

Yetkin, M., and M. Berber. "Robustness analysis using the measure of external reliability for multiple outliers." Survey Review 45, no. 330 (May 2013): 215–19. http://dx.doi.org/10.1179/1752270612y.0000000026.

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44

Karakaya, Fahri, and Abhrawashyu Awasthi. "Robustness and sensitivity of conjoint analysis versus multiple linear regression analysis." International Journal of Data Analysis Techniques and Strategies 6, no. 2 (2014): 121. http://dx.doi.org/10.1504/ijdats.2014.062461.

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45

Twyman, Nathan W., Jeffrey Gainer Proudfoot, Ryan M. Schuetzler, Aaron C. Elkins, and Douglas C. Derrick. "Robustness of Multiple Indicators in Automated Screening Systems for Deception Detection." Journal of Management Information Systems 32, no. 4 (October 2, 2015): 215–45. http://dx.doi.org/10.1080/07421222.2015.1138569.

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46

Shi, Changhong, Han‐xiong Li, and Tianshou Zhou. "Architecture‐dependent robustness in a class of multiple positive feedback loops." IET Systems Biology 7, no. 1 (February 2013): 1–10. http://dx.doi.org/10.1049/iet-syb.2011.0090.

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47

Chan, Nathan. "Robustness of the multiple realization method for stochastic hydraulic aquifer management." Water Resources Research 29, no. 9 (September 1993): 3159–67. http://dx.doi.org/10.1029/93wr01410.

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48

Hatanaka, T., and T. Tachibana. "Robustness based low-energy multiple routing configurations for fast failure recovery." Journal of Physics: Conference Series 1195 (April 2019): 012015. http://dx.doi.org/10.1088/1742-6596/1195/1/012015.

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49

Nascimento, J. C., and J. S. Marques. "Improving the robustness of parametric shape tracking with switched multiple models." Pattern Recognition 35, no. 12 (December 2002): 2711–18. http://dx.doi.org/10.1016/s0031-3203(01)00244-8.

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50

Xiao, Hanshen, Yufeng Huang, Yu Ye, and Guoqiang Xiao. "Robustness in Chinese Remainder Theorem for Multiple Numbers and Remainder Coding." IEEE Transactions on Signal Processing 66, no. 16 (August 15, 2018): 4347–61. http://dx.doi.org/10.1109/tsp.2018.2846228.

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