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Relevant bibliographies by topics / Robust methods

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Academic literature on the topic 'Robust methods'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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Journal articles on the topic "Robust methods"

1

Hettmansperger, Thomas P., Joseph W. McKean, and Simon J. Sheather. "Robust Nonparametric Methods." Journal of the American Statistical Association 95, no. 452 (2000): 1308–12. http://dx.doi.org/10.1080/01621459.2000.10474337.

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Künsch, Hans R. "Robust Methods for Credibility." ASTIN Bulletin 22, no. 1 (1992): 33–49. http://dx.doi.org/10.2143/ast.22.1.2005125.

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AbstractExcess claims lead to an unsatisfactory behavior of standard linear credibility estimators. We suggest in this paper to use robust methods in order to obtain better estimators. Our first proposal is the linear credibility estimator with the claims replaced by a robust M-estimator of scale calculed from the claims. This corresponds to a truncation of the claims with a truncation point depending on the data and different for each contract. We discuss the properties of the robust M-estimator and present several examples. In order to improve the performance for a very small number of years

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Dennis, Richard, Kai Leitemo, and Ulf Söderström. "Methods for robust control." Journal of Economic Dynamics and Control 33, no. 8 (2009): 1604–16. http://dx.doi.org/10.1016/j.jedc.2009.02.011.

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Mi, Jie. "Robust Nonparametric Statistical Methods." Technometrics 41, no. 1 (1999): 74. http://dx.doi.org/10.1080/00401706.1999.10485600.

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Stoimenova, Eugenia. "Robust nonparametric statistical methods." Journal of Applied Statistics 39, no. 6 (2012): 1383–84. http://dx.doi.org/10.1080/02664763.2012.657414.

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Wilcox, Rand R., Simon Sheather, Edgar Brunner, and Michael G. Schimek. "Nonparametric and Robust Methods." Computational Statistics & Data Analysis 51, no. 10 (2007): 5010–12. http://dx.doi.org/10.1016/j.csda.2006.10.026.

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Hogg, Robert V., and William J. J. Rey. "Introduction to Robust and Quasi-Robust Statistical Methods." Journal of the American Statistical Association 80, no. 391 (1985): 784. http://dx.doi.org/10.2307/2288518.

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Rota, Gian-Carlo. "Introduction to robust and quasi-robust statistical methods." Advances in Mathematics 60, no. 1 (1986): 123. http://dx.doi.org/10.1016/0001-8708(86)90012-5.

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Hubert, Mia, Peter J. Rousseeuw, and Stefan Van Aelst. "High-Breakdown Robust Multivariate Methods." Statistical Science 23, no. 1 (2008): 92–119. http://dx.doi.org/10.1214/088342307000000087.

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Tyler, David E. "Robust Statistical Methods with R." Journal of the American Statistical Association 102, no. 478 (2007): 759–60. http://dx.doi.org/10.1198/jasa.2007.s187.

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Dissertations / Theses on the topic "Robust methods"

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Peel, Vincent Robert. "Robust methods for robust passive sonar." Thesis, University of Southampton, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.305876.

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Helmersson, Anders. "Methods for robust gain scheduling." Doctoral thesis, Linköpings universitet, Reglerteknik, 1995. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-75513.

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Regulatorer används i många tillämpningar för att förbättra prestanda ochegenskaper hos fordon och farkoster av olika slag, t ex flygplan och raketer.Beroende på olika faktorer, som hastighet och höjd, kan dynamiken ochuppträdandet av dessa farkoster variera. Man vill därför låta regulatorn tahänsyn till dessa variationer genom att ändra sitt uppförande i motsvarandegrad. Regulatorn kan konstrueras på olika sätt. En möjlighet är att styrsignalernaberor linjärt eller proportionellt på regulatorns insignaler, till exempelavvikelser från önskad bana. Regulatorn bestäms av ett antal parametrarsom

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Nargis, Suraiya, and n/a. "Robust methods in logistic regression." University of Canberra. Information Sciences & Engineering, 2005. http://erl.canberra.edu.au./public/adt-AUC20051111.141200.

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My Masters research aims to deepen our understanding of the behaviour of robust methods in logistic regression. Logistic regression is a special case of Generalized Linear Modelling (GLM), which is a powerful and popular technique for modelling a large variety of data. Robust methods are useful in reducing the effect of outlying values in the response variable on parameter estimates. A literature survey shows that we are still at the beginning of being able to detect extreme observations in logistic regression analyses, to apply robust methods in logistic regression and to present informativel

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Mutapcic, Almir. "Robust optimization : methods and applications /." May be available electronically:, 2008. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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Naish-Guzman, Andrew Guillermo Peter. "Sparse and robust kernel methods." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612420.

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Mwitondi, K. S. "Robust methods in data mining." Thesis, University of Leeds, 2003. http://etheses.whiterose.ac.uk/807/.

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The thesis focuses on two problems in Data Mining, namely clustering, an exploratory technique to group observations in similar groups, and classification, a technique used to assign new observations to one of the known groups. A thorough study of the two problems, which are also known in the Machine Learning literature as unsupervised and supervised classification respectively, is central to decision making in different fields - the thesis seeks to contribute towards that end. In the first part of the thesis we consider whether robust methods can be applied to clustering - in particular, we p

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Huang, Shu-Pang. "ROBUST METHODS FOR ESTIMATING ALLELE FREQUENCIES." NCSU, 2001. http://www.lib.ncsu.edu/theses/available/etd-20010614-213208.

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<p>HUANG, SHU-PANG. ROBUST METHODS FOR ESTIMATING ALLELE FREQUENCIES (Advisor: Bruce S. Weir) The distribution of allele frequencies has beena major focus in population genetics. Classical approaches usingstochastic arguments depend highly on the choice of mutationmodel. Unfortunately, it is hard to justify which mutation modelis suitable for a particular sample. We propose two methods toestimate allele frequencies, especially for rare alleles, withoutassuming a mutation model. The first method achieves its goalthrough two steps. First it estimates the number of alleles in apopulation using a

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Feng, Chunlin, and 馮淳林. "Robust estimation methods for image matching." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B29752693.

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Er, Fikret. "Robust methods in statistical shape analysis." Thesis, University of Leeds, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342394.

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Kudo, Jun S. M. Massachusetts Institute of Technology. "Robust adaptive high-order RANS methods." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/95563.

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Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 89-94).<br>The ability to achieve accurate predictions of turbulent flow over arbitrarily complex geometries proves critical in the advancement of aerospace design. However, quantitatively accurate results from modern Computational Fluid Dynamics (CFD) tools are often accompanied by intractably high computational expenses and are significantly hindered by the lack of automation. In particular, the gene

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Books on the topic "Robust methods"

1

1944-, McKean Joseph W., ed. Robust nonparametric statistical methods. Arnold, 1998.

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Hackbusch, Wolfgang, ed. Robust Multi-Grid Methods. Vieweg+Teubner Verlag, 1989. http://dx.doi.org/10.1007/978-3-322-86200-6.

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Heritier, Stephane, Eva Cantoni, Samuel Copt, and Maria-Pia Victoria-Feser. Robust Methods in Biostatistics. John Wiley & Sons, Ltd, 2009. http://dx.doi.org/10.1002/9780470740538.

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Andersen, Robert. Modern methods for robust regression. Sage Pub., 2006.

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Modern methods for robust regression. Sage Publications, 2008.

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1965-, Picek Jan, ed. Robust statistical methods with R. Chapman & Hall/CRC, 2006.

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JureÚcková, Jana. Robust statistical methods with R. Chapman & Hall/CRC, 2006.

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Andersen, Robert. Modern Methods for Robust Regression. SAGE Publications, Inc., 2008. http://dx.doi.org/10.4135/9781412985109.

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Svetozar, Margenov, ed. Robust algebraic multilevel methods and algorithms. Walter De Gruyter, 2009.

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Nordhausen, Klaus, and Sara Taskinen, eds. Modern Nonparametric, Robust and Multivariate Methods. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22404-6.

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Book chapters on the topic "Robust methods"

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Meer, Peter, and Sushil Mittal. "Robust Methods." In Computer Vision. Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_650.

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Maronna, Ricardo. "Robust Statistical Methods." In International Encyclopedia of Statistical Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_496.

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Thorburn, Daniel. "Robust Bayesian Methods." In Probability and Bayesian Statistics. Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-1885-9_47.

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Cavazzuti, Marco. "Robust Design Analysis." In Optimization Methods. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31187-1_6.

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Mackenroth, Uwe. "Classical Design Methods." In Robust Control Systems. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-09775-5_4.

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Wilcox, Rand R. "Robust Regression." In Fundamentals of Modern Statistical Methods. Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3522-2_11.

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Wilcox, Rand R. "Robust Regression." In Fundamentals of Modern Statistical Methods. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-5525-8_11.

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Amato, Francesco, Massimiliano Mattei, Stefano Scala, and Leopoldo Verde. "Design via LQ methods." In Robust Flight Control. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0113872.

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Walczak, Beata, MichaŁ Daszykowski, and Ivana Stanimirova. "Robust Methods in Qsar." In Challenges and Advances in Computational Chemistry and Physics. Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-1-4020-9783-6_6.

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Katebi, Reza. "Robust Multivariable Tuning Methods." In PID Control in the Third Millennium. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2425-2_9.

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Conference papers on the topic "Robust methods"

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Kitchenham, Barbara. "Robust statistical methods." In EASE '15: 19th International Conference on Evaluation and Assessment in Software Engineering. ACM, 2015. http://dx.doi.org/10.1145/2745802.2747956.

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Bloem, Roderick, Karin Greimel, Thomas A. Henzinger, and Barbara Jobstmann. "Synthesizing robust systems." In 2009 Formal Methods in Computer-Aided Design (FMCAD). IEEE, 2009. http://dx.doi.org/10.1109/fmcad.2009.5351139.

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Du, Xiaoping, Yijun Wang, and Wei Chen. "Methods for robust multidisciplinary design." In 41st Structures, Structural Dynamics, and Materials Conference and Exhibit. American Institute of Aeronautics and Astronautics, 2000. http://dx.doi.org/10.2514/6.2000-1785.

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Frey, Daniel D., and Xiang Li. "Validating Robust-Parameter-Design Methods." In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASME, 2004. http://dx.doi.org/10.1115/detc2004-57518.

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Kekec, Taygun, and David M. J. Tax. "Robust Gram Embeddings." In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/d16-1113.

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Hiroyasu, Tomoyuki, and Hiroshi Yamakawa. "Effective Design Methods of Wide Robust Structures." In ASME 1996 Design Engineering Technical Conferences and Computers in Engineering Conference. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/96-detc/dac-1066.

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Abstract A structure, whose characteristics are less modified by any changes of external forces, boundary conditions and productive errors, has advantages for its practical use. This character is called robustness. In the consecutive study (Yamakawa and Miyashita, 1991), the authors studied the structural design with robustness and proposed a new structural concept called “Robust Structure.” Robust structural design is more useful when the structures are designed in the upper flow of the design process than in the lower flow. Robust structures can be designed by minimizing the sensitivities of

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Dai, Zhihuang, Michael J. Scott, and Zissimos P. Mourelatos. "Robust Design Using Preference Aggregation Methods." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/dac-48715.

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Robust design is a methodology for improving the quality of a product or process by minimizing the effect of variations in the inputs without eliminating the causes of those variations. In robust design, the best design is obtained by solving a multicriteria optimization problem, trading off the nominal performance against the minimization of the variation of the performance measure. Because these methods often combine the two criteria with a weighted sum or another fixed aggregation strategy, which are known to miss Pareto points, they may fail to obtain a desired design. To overcome this ina

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TOLLEY, H., and E. JONES. "Robust methods in estimating gravity anomalies." In Astrodynamics Conference. American Institute of Aeronautics and Astronautics, 1986. http://dx.doi.org/10.2514/6.1986-2259.

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Ash, Joshua N., and Lee C. Potter. "Robust system multiangulation using subspace methods." In the 6th international conference. ACM Press, 2007. http://dx.doi.org/10.1145/1236360.1236369.

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Ash, Joshua N., and Lee C. Potter. "Robust System Multiangulation Using Subspace Methods." In 2007 6th International Symposium on Information Processing in Sensor Networks. IEEE, 2007. http://dx.doi.org/10.1109/ipsn.2007.4379665.

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Reports on the topic "Robust methods"

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Safonov, Michael G., and Edmond A. Jonckheere. Practical Methods for Robust Multivariable Control. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada215487.

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Ellison, Charlotte, Zachary Roth, and Crystal Chen. Robust methods for fusing heterogeneous spatiotemporal data. Engineer Research and Development Center (U.S.), 2019. http://dx.doi.org/10.21079/11681/34755.

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Shamma, Jeff S. Set-Valued Methods for Robust Nonlinear Control. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada383800.

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Safonov, Michael G. Robust Control, Feedback and Learning: Data-Driven Methods. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada427715.

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Sweriduk, G. D., P. K. Menon, and M. L. Stienberg. Robust Command Augmentation System Design Using Genetic Methods. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada350849.

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Halverson, Don R. Geometric Methods with Application to Robust Detection and Estimation. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada206999.

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Jiang, Chengyang, William Goddard, III, Andres Jaramillo-Botero, and Qisheng (John) Ma. Robust Molecular Predictive Methods for Novel Polymer Discovery and Applications. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1761208.

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Risson, J., and T. Moors. Survey of Research towards Robust Peer-to-Peer Networks: Search Methods. RFC Editor, 2007. http://dx.doi.org/10.17487/rfc4981.

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Abate, M. L., M. C. Morrow, and T. Kuczek. An application of robust parameter design using an alternative to Taguchi methods. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/399996.

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Helton, J. W. Design of Robust Controllers: Frequency Domain Methods and Their Non-Linear Extensions. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada380889.

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