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Journal articles on the topic 'Robust methods'

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1

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

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2

Künsch, Hans R. "Robust Methods for Credibility." ASTIN Bulletin 22, no. 1 (May 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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3

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

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4

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

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5

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

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6

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

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7

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 (September 1985): 784. http://dx.doi.org/10.2307/2288518.

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8

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

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9

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

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10

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

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11

Tyler, David E. "Robust Statistics: Theory and Methods." Journal of the American Statistical Association 103, no. 482 (June 1, 2008): 888–89. http://dx.doi.org/10.1198/jasa.2008.s239.

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12

Olive, David J. "Robust Statistical Methods With R." Technometrics 49, no. 4 (November 2007): 496. http://dx.doi.org/10.1198/tech.2007.s692.

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13

Kay, Herbert. "Robust Identification Using Semiquantitative Methods." IFAC Proceedings Volumes 30, no. 18 (August 1997): 277–82. http://dx.doi.org/10.1016/s1474-6670(17)42414-1.

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14

Scales, J. A., and A. Gersztenkorn. "Robust methods in inverse theory." Inverse Problems 4, no. 4 (October 1, 1988): 1071–91. http://dx.doi.org/10.1088/0266-5611/4/4/010.

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15

Burer, Samuel, and Veronica Piccialli. "Three methods for robust grading." European Journal of Operational Research 272, no. 1 (January 2019): 364–71. http://dx.doi.org/10.1016/j.ejor.2018.06.019.

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16

Atkinson, Anthony C., Marco Riani, and Francesca Torti. "Robust methods for heteroskedastic regression." Computational Statistics & Data Analysis 104 (December 2016): 209–22. http://dx.doi.org/10.1016/j.csda.2016.07.002.

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17

Zhang, Juan Juan, and Yong Hui Ge. "More Efficient Methods among Commonly Used Robust Estimation Methods for Robust Unitary Linear Regression." Advanced Materials Research 712-715 (June 2013): 2493–96. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.2493.

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Robust regression, based on the robust estimation, can effectively eliminate or weaken the influences of outliers. However, different robust methods have different abilities to eliminate or weaken the influences of outliers. The current paper employs simulation experiments, taking unitary linear regressions with different numbers of observations, different numbers of gross errors and different values of gross errors as examples, to compare the robustness of 13 commonly used robust estimation methods by the average values of relative gains. The results indicate that the L1 and GermanMcClure methods are relatively more efficient for unitary linear regression. They can more efficiently eliminate or weaken the influences of gross errors on regression coefficient valuation than other robust estimation methods.
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18

Kalina, Jan. "On Multivariate Methods in Robust Econometrics." Prague Economic Papers 21, no. 1 (January 1, 2012): 69–82. http://dx.doi.org/10.18267/j.pep.411.

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19

Jan, Kalina. "Highly robust methods in data mining." Serbian Journal of Management 8, no. 1 (2013): 9–24. http://dx.doi.org/10.5937/sjm8-3226.

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20

Monzón, Nelson, Agustín Salgado, and Javier Sánchez. "Robust Discontinuity Preserving Optical Flow Methods." Image Processing On Line 5 (November 7, 2016): 165–82. http://dx.doi.org/10.5201/ipol.2016.172.

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21

Veselý, Vojtech. "Robust Control Methods a Systematic Survey." Journal of Electrical Engineering 64, no. 1 (January 1, 2013): 59–64. http://dx.doi.org/10.2478/jee-2013-0009.

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The paper addresses the problem how to recognize a level of robust controller design and is aimed show the difficulties of implementation for practical use. In the first part of paper we introduce the survey of robust controller design for SISO systems with generalization design procedure for structured and unstructured uncertainties. The second part of paper is devoted to MIMO systems. In the frequency domain robust controller design procedure we reduce to independent design of SISO subsystems and in time domain the LMI or BMI approaches with polytopic system description are favorable.
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22

Theune, D., R. Thiele, W. John, and T. Lengauer. "Robust methods for EMC-driven routing." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 13, no. 11 (1994): 1366–78. http://dx.doi.org/10.1109/43.329265.

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23

Sánchez Peña, Ricardo S. "Robust control design using H∞ methods." Automatica 38, no. 11 (November 2002): 2035–36. http://dx.doi.org/10.1016/s0005-1098(02)00106-1.

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24

Nagarajan, S. S., J. P. Owen, L. B. Hinkley, H. T. Attias, and K. Sekihara. "Robust methods for estimating functional connectivity." NeuroImage 47 (July 2009): S169. http://dx.doi.org/10.1016/s1053-8119(09)71816-6.

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25

Shoemaker, Anne C., Kwok-Leung Tsui, and C. F. Jeff Wu. "Economical Experimentation Methods for Robust Design." Technometrics 33, no. 4 (November 1991): 415–27. http://dx.doi.org/10.1080/00401706.1991.10484870.

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26

Altland, Henry W. "Engineering Methods for Robust Product Design." Technometrics 38, no. 3 (August 1996): 286–87. http://dx.doi.org/10.1080/00401706.1996.10484511.

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27

Hirshberg, D., and N. Merhav. "Robust methods for model order estimation." IEEE Transactions on Signal Processing 44, no. 3 (March 1996): 620–28. http://dx.doi.org/10.1109/78.489035.

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28

Faraone, Stephen V., and Donald D. Dorfman. "Lag sequential analysis: Robust statistical methods." Psychological Bulletin 101, no. 2 (1987): 312–23. http://dx.doi.org/10.1037/0033-2909.101.2.312.

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29

Pison, Greet, and Stefan Van Aelst. "Diagnostic Plots for Robust Multivariate Methods." Journal of Computational and Graphical Statistics 13, no. 2 (June 2004): 310–29. http://dx.doi.org/10.1198/1061860043498_a.

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30

Dave, R. N., and R. Krishnapuram. "Robust clustering methods: a unified view." IEEE Transactions on Fuzzy Systems 5, no. 2 (May 1997): 270–93. http://dx.doi.org/10.1109/91.580801.

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31

Kyrion, Tobias, and Graham Alldredge. "Robust inversion methods for aerosol spectroscopy." Inverse Problems in Science and Engineering 25, no. 5 (June 3, 2016): 710–48. http://dx.doi.org/10.1080/17415977.2016.1191075.

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32

Walsteijn, Fred H. "Robust Numerical Methods for 2D Turbulence." Journal of Computational Physics 114, no. 1 (September 1994): 129–45. http://dx.doi.org/10.1006/jcph.1994.1154.

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33

JIANG, HONG, and PETER A. FORSYTH. "ROBUST NUMERICAL METHODS FOR TRANSONIC FLOWS." International Journal for Numerical Methods in Fluids 24, no. 5 (March 15, 1997): 457–76. http://dx.doi.org/10.1002/(sici)1097-0363(19970315)24:5<457::aid-fld504>3.0.co;2-i.

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34

Møller, S. Frosch, J. von Frese, and R. Bro. "Robust methods for multivariate data analysis." Journal of Chemometrics 19, no. 10 (October 2005): 549–63. http://dx.doi.org/10.1002/cem.962.

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35

Mantzaflaris, Angelos. "Robust algebraic methods for geometric computing." ACM Communications in Computer Algebra 45, no. 3/4 (January 23, 2012): 237. http://dx.doi.org/10.1145/2110170.2110188.

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36

EKLUND, MATTHEW P., ALY A. FARAG, and MOUMEN T. EL-MELEGY. "ROBUST CORRESPONDENCE METHODS FOR STEREO VISION." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 07 (November 2003): 1059–79. http://dx.doi.org/10.1142/s0218001403002861.

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Correspondence is one of the major problems that must be solved in stereo vision. Correlation has been commonly used in the past for this problem. However, most classical linear correlation methods fail near depth discontinuities and in the presence of occlusions. Many robust methods have been proposed that claim to effectively deal with some or all of these issues. Many of these robust methods are transformation-based, however, other robust methods are non-transformation based. This paper gives five requirements that should be met by a transformation-based robust correlation method. We compare some of the robust correspondence methods and demonstrate their utility on different data sets. Based on these results, we propose a solution to the correspondence problem which represents a compromise between the speed of classical correlation and the improved results obtained from a more robust correspondence method. Also, we propose a median filtering technique that removes noise from the disparity maps while preserving certain image features usually removed by ordinary median filtering.
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37

Pishvaee, Mir Saman, and Mohamadreza Fazli Khalaf. "Novel robust fuzzy mathematical programming methods." Applied Mathematical Modelling 40, no. 1 (January 2016): 407–18. http://dx.doi.org/10.1016/j.apm.2015.04.054.

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38

Schnepper, C. A., and M. A. Stadtherr. "Robust process simulation using interval methods." Computers & Chemical Engineering 20, no. 2 (February 1996): 187–99. http://dx.doi.org/10.1016/0098-1354(95)00014-s.

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39

García-Escudero, Luis Angel, Alfonso Gordaliza, Carlos Matrán, and Agustín Mayo-Iscar. "A review of robust clustering methods." Advances in Data Analysis and Classification 4, no. 2-3 (June 18, 2010): 89–109. http://dx.doi.org/10.1007/s11634-010-0064-5.

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40

Lima, Luiz Renato, Marcelo Moreira, Jack Porter, and Zhijie Xiao. "Nonparametric and robust methods in econometrics." Journal of Econometrics 152, no. 2 (October 2009): 79–80. http://dx.doi.org/10.1016/j.jeconom.2009.04.001.

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41

Yuan, Ke‐Hai, and Brenna Gomer. "An overview of applied robust methods." British Journal of Mathematical and Statistical Psychology 74, S1 (January 29, 2021): 199–246. http://dx.doi.org/10.1111/bmsp.12230.

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42

Yuan, Yuan, and Yong Hui Ge. "More Efficient Methods among Commonly Used Robust Estimation Methods for Similarity Transformation." Advanced Materials Research 712-715 (June 2013): 2497–500. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.2497.

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mage calibration is a necessary step in image processing. Similarity transformation is a widely used image calibration method. Robust estimation methods are often used to eliminate or weaken the influences of gross errors on image calibration. However, different robust estimation methods have different capabilities in eliminating or weakening gross errors. The current paper employed simulation experiments using different coincident points and the number of gross errors included in the observations to compare the robustness of 13 commonly used robust estimation methods. Results indicated that L1 and GermanMcClure methods are relatively more efficient than other robust estimation methods for image calibration based on similarity transformation.
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43

Carroll, R. J., H. Bunke, and O. Bunke. "Nonlinear Regression, Functional Relations and Robust Methods." Biometrics 46, no. 3 (September 1990): 877. http://dx.doi.org/10.2307/2532110.

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44

Äyrämö, S., I. Pölönen, and M. A. Eskelinen. "CLUSTERING INCOMPLETE SPECTRAL DATA WITH ROBUST METHODS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W3 (October 19, 2017): 13–17. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w3-13-2017.

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Missing value imputation is a common approach for preprocessing incomplete data sets. In case of data clustering, imputation methods may cause unexpected bias because they may change the underlying structure of the data. In order to avoid prior imputation of missing values the computational operations must be projected on the available data values. In this paper, we apply a robust nan-K-spatmed algorithm to the clustering problem on hyperspectral image data. Robust statistics, such as multivariate medians, are more insensitive to outliers than classical statistics relying on the Gaussian assumptions. They are, however, computationally more intractable due to the lack of closed-form solutions. We will compare robust clustering methods on the bands incomplete data cubes to standard K-means with full data cubes.
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45

Chernobai, Anna, and Svetlozar Rachev. "Applying robust methods to operational risk modeling." Journal of Operational Risk 1, no. 1 (2006): 27–41. http://dx.doi.org/10.21314/jop.2006.003.

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46

Trzpiot, Grażyna. "Selected Robust Methods for Camp Model Estimation." Folia Oeconomica Stetinensia 12, no. 2 (December 1, 2012): 58–71. http://dx.doi.org/10.2478/v10031-012-0032-7.

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Abstract This paper presents evidence that Ordinary Least Squares estimators of beta coefficients of major firms and portfolios are highly sensitive to observations of extremes in market index returns. This sensitivity is rooted in the inconsistency of the quadratic loss function in financial theory. By introducing considerations of risk aversion into the estimation procedure using alternative estimators measures of variability we can overcome this lack of robustness and improve the reliability of the results.
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47

Lee, Guem Mi, Kyupil Yeon, and Hyeuk Kim. "A Comparative Study on Robust Regression Methods." IARJSET 3, no. 8 (August 20, 2016): 166–70. http://dx.doi.org/10.17148/iarjset.2016.3830.

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48

YILMAZ, İrem, and Fırat ÖZDEMİR. "Robust Methods for Comparing K Independent Groups." Turkiye Klinikleri Journal of Biostatistics 8, no. 2 (2016): 143–51. http://dx.doi.org/10.5336/biostatic.2016-50837.

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49

Victoria-Feser, Maria-Pia, and Elvezio Ronchetti. "Robust methods for personal-income distribution models." Canadian Journal of Statistics 22, no. 2 (June 1994): 247–58. http://dx.doi.org/10.2307/3315587.

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50

Penev, Spiridon, and Kanta Naito. "Locally robust methods and near-parametric asymptotics." Journal of Multivariate Analysis 167 (September 2018): 395–417. http://dx.doi.org/10.1016/j.jmva.2018.06.006.

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