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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"

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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, we propose a second estimator, which incorporates information from other claims into the M-estimator.

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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 kan ändras efter de yttre betingelserna (t ex hastighet och höjd för ett flygplan). Denna metod kallas för parameterstyrning. Vid konstruktionen av regulatorn använder man en modell av det systemsom ska styras. En viktig och önskad egenskap hos en regulator är att denska kunna fungera bra även om systemet den ska styra varierar eller avvikerfrån modellen. Ett flygplan beter sig på olika sätt beroende på hur mycketlast den har och hur lasten är placerad. Det är därför önskvärt att användaregulatorer som inte är känsliga för variationer, t ex i last, och som uppförsig bra i olika situationer. En sådan regulator sägs vara robust. Avhandlingen behandlar hur man kan analysera och konstruera regulatorersom är robusta och parameterstyrda. Det visar sig att båda dessaproblem är likartade och att de kan behandlas med samma metoder.<br>This thesis considers the analysis of systems with uncertainties and the design of controllers to such systems. Uncertainties are treated in a relatively broad sense covering gain-bounded elements that are not known a priori but could be available to the controller in real time. The uncertainties are in the most general case norm-bounded operators with a given block-diagonal structure. The structure includes parameters, linear time-invariant and time-varying systems as well as nonlinearities. In some applications the controller may have access to the uncertainty, e.g. a parameter that depends on some known condition. There exist well-known methods for determining stability of systems subject to uncertainties. This thesis is within the framework for structured singular values also denoted by μ. Given a certain class of uncertainties, μ is the inverse of the size of the smallest uncertainty that causes the system to become unstable. Thus, μ is a measure of the system's "structured gain". In general it is not possible to compute μ exactly, but an upper bound can be determined using efficient numerical methods based on linear matrix inequalities. An essential contribution in this thesis is a new synthesis algorithm for finding controllers when parametric (real) uncertainties are present. This extends previous results on μ synthesis involving dynamic (complex) uncertainties. Specifically, we can design gain scheduling controllers using the new μ synthesis theorem, with less conservativeness than previous methods. Also, algorithms for model reduction of uncertainty systems are given. A gain scheduling controller is a linear regulator whose parameters are changed as a function of the varying operating conditions. By treating nonlinearities as uncertainties, μ methods can be used in gain scheduling design. In the discussion, emphasis is put on how to take into consideration different characteristics of the time-varying properties of the system to be controlled. Also robustness and its relation with gain scheduling are treated. In order to handle systems with time-invariant uncertainties, both linear systems and constant parameters, a set of scalings and multipliers are introduced. These are matched to the properties of the uncertainties. Also, multipliers for treating uncertainties that are slowly varying, such that the rate of change is bounded, are introduced. Using these multipliers the applicability of the analysis and synthesis results are greatly extended.

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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 informatively the results of logistic regression analyses. In Chapter 1 I have made a basic introduction to logistic regression, with an example, and to robust methods in general. In Chapters 2 through 4 of the thesis I have described traditional methods and some relatively new methods for presenting results of logistic regression using powerful visualization techniques as well as the concepts of outliers in binomial data. I have used different published data sets for illustration, such as the Prostate Cancer data set, the Damaged Carrots data set and the Recumbent Cow data set. In Chapter 4 I summarize and report on the modem concepts of graphical methods, such as central dimension reduction, and the use of graphics as pioneered by Cook and Weisberg (1999). In Section 4.6 I have then extended the work of Cook and Weisberg to robust logistic regression. In Chapter 5 I have described simulation studies to investigate the effects of outlying observations on logistic regression (robust and non-robust). In Section 5.2 I have come to the conclusion that, in the case of classical or robust multiple logistic regression with no outliers, robust methods do not necessarily provide more reasonable estimates of the parameters for the data that contain no st~ong outliers. In Section 5.4 I have looked into the cases where outliers are present and have come to the conclusion that either the breakdown method or a sensitivity analysis provides reasonable parameter estimates in that situation. Finally, I have identified areas for further study.

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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 perform clustering on fuzzy data using two methods originally developed for outlier-detection. The fuzzy data clusters are characterised by two intersecting lines such that points belonging to the same cluster lie close to the same line. This part of the thesis also investigates a new application of finite mixture of normals to the fuzzy data problem. The second part of the thesis addresses issues relating to classification - in particular, classification trees and boosting. The boosting algorithm is a relative newcomer to the classification portfolio that seeks to enhance the performance of classifiers by iteratively re-weighting the data according to their previous classification status. We explore the performance of "boosted" trees (mainly stumps) based on 3 different models all characterised by a sine-wave boundary. We also carry out a thorough study of the factors that affect the boosting algorithm. Other results include a new look at the concept of randomness in the classification context, particularly because the form of randomness in both training and testing data has directly affects the accuracy and reliability of domain- partitioning rules. Further, we provide statistical interpretations of some of the classification-related concepts, originally used in Computer Science, Machine Learning and Artificial Intelligence. This is important since there exists a need for a unified interpretation of some of the "landmark" concepts in various disciplines, as a step forward towards seeking the principles that can guide and strengthen practical applications.

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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 sample coverage method and then models rankedfrequencies for these alleles using the stretchedexponential/Weibull distribution. Simulation studies have shownthat both steps are robust to different mutation models. Thesecond method uses Bayesian approach to estimate both the numberof alleles and their frequencies simultaneously by assuming anon-informative prior distribution. The Bayesian approach is alsorobust to mutation models. Questions concerning the probability offinding a new allele, and the possible highest (or lowest)probability for a new-found allele can be answered by bothmethods. The advantages of our approaches include robustness tomutation model and ability to be easily extended to genotypic,haploid and protein structure data.<P>

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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 generation of a suitable mesh for a given flow problem often requires significant amounts of human input. This process however encounters difficulties for turbulent flows which exhibit a wide range of length scales that must be spatially resolved for an accurate solution. Higher-order adaptive methods are attractive candidates for addressing these deficiencies by promising accurate solutions at a reduced cost in a highly automated fashion. However, these methods in general are still not robust enough for industrial applications and significant advances must be made before the true realization of robust automated three-dimensional turbulent CFD. This thesis presents steps towards this realization of a robust high-order adaptive Reynolds-Averaged Navier-Stokes (RANS) method for the analysis of turbulent flows. Specifically, a discontinuous Galerkin (DG) discretization of the RANS equations and an output-based error estimation with an associated mesh adaptation algorithm is demonstrated. To improve the robustness associated with the RANS discretization, modifications to the negative continuation of the Spalart-Allmaras turbulence model are reviewed and numerically demonstrated on a test case. An existing metric-based adaptation framework is adopted and modified to improve the procedure's global convergence behavior. The resulting discretization and modified adaptation procedure is then applied to two-dimensional and three-dimensional turbulent flows to demonstrate the overall capability of the method.<br>by Jun Kudo.<br>S.M.

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Books on the topic "Robust methods"

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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 the reference function relative to the variable parameters. However, the sensitivities provide only the local information even when their values are evaluated. Therefore, it is not clarified whether robust structure designed by the sensitivities has wide robustness or not. To conquer this problem, this paper shows robust structural design method using the fuzzy sets. In this method, the variable parameters are shown with fuzzy sets and then obtain the wide robustness. It is also pointed out that robust structural design using sensitivities can design wide robust structure in certain cases. Because robust structural design using fuzzy sets needs a lot of numerical iterations, it is important to figure out the effective design method of wide robust structures. In this paper, an idea is proposed how to design wide robust structures. The numerical examples designed by two different methods using sensitivities and fuzzy sets are shown. Further more, the proposed idea is discussed with these examples.

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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 inadequacy, a more comprehensive preference aggregation method is combined into robust design. Two examples are presented to illustrate the effectiveness of the proposed method.

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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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