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Robust Statistics
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A tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,723
Principal component analysis: A review and recent developments
Jolliffe, I. T., & Cadima, J. (2016) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2015.0202 · Cited by 10,117
Robust principal component analysis?
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Secure spread spectrum watermarking for multimedia
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Enhancing Sparsity by Reweighted ℓ 1 Minimization
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Parallel Tracking and Mapping for Small AR Workspaces
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Dose-Response Analysis Using R
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Least Median of Squares Regression
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Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
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The Human Serum Metabolome (HUSERMET) Consortium, Dunn, W. B., Broadhurst, D., Begley, P., Zelena, E., Francis-McIntyre, S., Anderson, N., Brown, M., Knowles, J. D., Halsall, A., et al. (2011) · Nature Protocols · doi:10.1038/nprot.2011.335 · Cited by 3,294
Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age
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Automatic Panoramic Image Stitching using Invariant Features
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Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not
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“In fact, the debate over squared versus absolute error terms had emerged, was subsequently forgotten, and reemerged over the preceding two centuries (Boscovich, 1757; Gauss, 1816; Laplace, 1818; Eddington, 1914; Fisher, 1920), with history given by (Stigler, 1973, 1984), making it one of the oldest questions in statis…”
BackgroundResultFast and Robust Multiframe Super Resolution
Farsiu, S., Robinson, M. D., Elad, M., & Milanfar, P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2004.834669 · Cited by 2,099
Least squares formulation of robust non-negative factor analysis
Paatero, P. (1997) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(96)00044-5 · Cited by 1,980
Alternatives to the Median Absolute Deviation
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A better lemon squeezer? maximum-likelihood regression with beta-distributed dependent variables.
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Correntropy: Properties and Applications in Non-Gaussian Signal Processing
Liu, W., Pokharel, P. P., & Príncipe, J. C. (2007) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2007.896065 · Cited by 1,720
Detecting differential usage of exons from RNA-seq data
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Assessment and Propagation of Model Uncertainty
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SNR Walls for Signal Detection
Tandra, R., & Sahai, A. (2008) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2007.914879 · Cited by 1,558
Measuring phenological variability from satellite imagery
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Kernel-based methods for hyperspectral image classification
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Feature extraction from faces using deformable templates
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Dictionaries for Sparse Representation Modeling
Rubinstein, R., Bruckstein, A. M., & Elad, M. (2010) · Proceedings of the IEEE · doi:10.1109/jproc.2010.2040551 · Cited by 1,318
Volatility forecast comparison using imperfect volatility proxies
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Principal Component Analysis: A Method for Determining the Essential Dynamics of Proteins
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The Selection of Prior Distributions by Formal Rules
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OSQP: an operator splitting solver for quadratic programs
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On Choosing and Bounding Probability Metrics
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SMOOTH TRANSITION AUTOREGRESSIVE MODELS — A SURVEY OF RECENT DEVELOPMENTS
van Dijk, D., Teräsvirta, T., & Franses, P. H. (2002) · Econometric Reviews · doi:10.1081/etc-120008723 · Cited by 1,199
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Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods
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Some aspects of the spline smoothing approach to non-parametric regression curve fitting
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K‐means clustering: A half‐century synthesis
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Multivariate Estimation with High Breakdown Point
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Robust statistical methods in R using the WRS2 package
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“As an example, Huber (1981) proposed the following function: Ψ(x) = { x if |x| ≤ K Ksign(x) if |x| > K (2) K is the bending constant for which Huber proposed a value of K = 1.28.”
MethodInfluentialOn the computational architecture of the neocortex: II The role of cortico-cortical loops
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Efficient region tracking with parametric models of geometry and illumination
Hager, G. D., & Belhumeur, P. N. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.722606 · Cited by 1,066
Robust Statistical Modeling Using the t Distribution
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Robust Regression by Means of S-Estimators
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Robust Portfolio Selection Problems
Goldfarb, D., & Iyengar, G. (2003) · Mathematics of Operations Research · doi:10.1287/moor.28.1.1.14260 · Cited by 898
Distributed optimization in sensor networks
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Power of One Bit of Quantum Information
Knill, E. H., & Laflamme, R. (1998) · Physical Review Letters · doi:10.1103/physrevlett.81.5672 · Cited by 918
A generalized Gaussian image model for edge-preserving MAP estimation
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The security of machine learning
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A regression-based approach to short-term system load forecasting
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First observation of PeV-energy neutrinos with IceCube.
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Robust techniques for signal processing: A survey
Kassam, S. A., & Poor, H. V. (1985) · Proceedings of the IEEE · doi:10.1109/proc.1985.13167 · Cited by 820
Robust statistics for outlier detection
Rousseeuw, P. J., & Mia Hubert, B. (2011) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.2 · Cited by 807
A Fast Approximation of the Bilateral Filter Using a Signal Processing Approach
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Upper Gastrointestinal (GI) pH in Young, Healthy Men and Women
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Intrinsic Statistics on Riemannian Manifolds: Basic Tools for Geometric Measurements
Pennec, X. (2006) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-006-6228-4 · Cited by 752
Pose estimation for augmented reality: a hands-on survey
Marchand, E., Uchiyama, H., & Spindler, F. (2016) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2015.2513408 · Cited by 746
The Development and Comparison of Robust Methods for Estimating the Fundamental Matrix
Torr, P., & Murray, D. (1997) · International Journal of Computer Vision · doi:10.1023/a:1007927408552 · Cited by 776
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Multisensor integration and fusion in intelligent systems
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Keystroke dynamics as a biometric for authentication
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Optimization Transfer Using Surrogate Objective Functions
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The possibilistic C-means algorithm: insights and recommendations
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The Effect of Family Control on Firm Value and Performance: Evidence from Continental Europe
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Robust clustering methods: a unified view
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The identification of multiple outliers
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A General and Adaptive Robust Loss Function
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USAC: A Universal Framework for Random Sample Consensus
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Maximum Correntropy Criterion for Robust Face Recognition
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Robust estimation of geomagnetic transfer functions
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Visual tracking and recognition using appearance-adaptive models in particle filters
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Natural disasters and population mobility in Bangladesh
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“The robustness concept has already been used for some time in biology (Kitano 2004), engineering (Carlson and Doyle 2002), and statistics (Huber 1981), and social scientists have become increasingly aware of how robustness may also be a key property of human behaviour and social systems (Anderies…”
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Robust regression methods for computer vision: A review
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Pose estimation from corresponding point data
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Advances and challenges in super‐resolution
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A Framework for Robust Subspace Learning
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Majorization–Minimization Algorithms for Wavelet-Based Image Restoration
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Metadata
- Authors
- Huber, P. J.
- Corresponding author
- Huber, Peter J.
- Venue
- Wiley Series in Probability and Statistics
- Publisher
- Wiley
- Published
- 1981-02-19
- ISSN
- 1940-6347 (ISSN-L)
- OpenAlex
- W2489822048
- Cited by
- 7,066
- References
- 0
- Citations by year
- 2026: 174 · 2025: 176 · 2024: 120 · 2023: 126 · 2022: 186 · 2021: 192 · 2020: 193 · 2019: 218 · 2018: 232 · 2017: 205 · 2016: 195 · 2015: 204 · 2014: 279 · 2013: 247 · 2012: 247
- FWCI
- 21.12
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Wiley series in probability and statistics (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Keywords
- robust statistics, outliers, influence function, robust estimation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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