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Items 201–300 of 1,011
Optimal motion and structure estimation
Weng, J., Ahuja, N., & Huang, T. S. (1993) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.232074 · Cited by 269
Applications of entropic spanning graphs
Hero, A. O., Ma, B., Michel, O., & Gorman, J. (2002) · IEEE Signal Processing Magazine · doi:10.1109/msp.2002.1028355 · Cited by 265
Airport capacity: representation, estimation, optimization
Gilbo, E. (1993) · IEEE Transactions on Control Systems Technology · doi:10.1109/87.251882 · Cited by 268
On three-dimensional surface reconstruction methods
Bolle, R. M., & Vemuri, B. C. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.67626 · Cited by 265
Fluid experimental flow estimation based on an optical-flow scheme
Corpetti, T., Heitz, D., Arroyo, G., Mémin, É., & Cruz, A. S. (2005) · Experiments in Fluids · doi:10.1007/s00348-005-0048-y · Cited by 268
Robust adaptive Metropolis algorithm with coerced acceptance rate
Vihola, M. (2011) · Statistics and Computing · doi:10.1007/s11222-011-9269-5 · Cited by 267
Asymptotic analysis of stochastic programs
Shapiro, A. (1991) · Annals of Operations Research · doi:10.1007/bf02204815 · Cited by 258
Action Localization with Tubelets from Motion
Jain, M., van Gemert, J. C., Jeǵou, H., Bouthémy, P., & Snoek, C. G. M. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.100 · Cited by 250
Robust Recognition Using Eigenimages
Leonardis, A., & Bischof, H. (2000) · Computer Vision and Image Understanding · doi:10.1006/cviu.1999.0830 · Cited by 266
A Splitting-Based Iterative Algorithm for Accelerated Statistical X-Ray CT Reconstruction
Ramani, S., & Fessler, J. A. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2175233 · Cited by 257
Choice of estimator for distribution system state estimation
Singh, R., Pal, B. C., & Jabr, R. A. (2009) · IET Generation Transmission & Distribution · doi:10.1049/iet-gtd.2008.0485 · Cited by 262
Combining Edge and Texture Information for Real-Time Accurate 3D Camera Tracking
Vacchetti, L., Lepetit, V., & Fua, P. (2005) · Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR · doi:10.1109/ismar.2004.24 · Cited by 265
On the Applications of Robust PCA in Image and Video Processing
Bouwmans, T., Javed, S., Zhang, H., Lin, Z., & Otazo, R. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2853589 · Cited by 258
High-breakdown robust multivariate methods
Mia Hubert, B., Rousseeuw, P. J., & Aelst, S. V. (2008) · Ghent University Academic Bibliography (Ghent University) · Cited by 256
Johnson‐Roberson, M., Pizarro, O. R., Williams, S. B., & Mahon, I. (2009) · Journal of Field Robotics · doi:10.1002/rob.20324 · Cited by 263
Regularized Robust Coding for Face Recognition
Yang, M., Lei Zhang, A., Yang, J., & Zhang, D. D. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2235849 · Cited by 266
Desertification alters patterns of aboveground net primary production in Chihuahuan ecosystems
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Anomaly detection by robust statistics
Rousseeuw, P. J., & Hubert, M. (2017) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1236 · Cited by 260
Sardy, S., Tseng, P., & Bruce, A. (2001) · IEEE Transactions on Signal Processing · doi:10.1109/78.923297 · Cited by 254
Robust clustering with applications in computer vision
Jolion, J.-M., Meer, P., & Bataouche, S. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.85669 · Cited by 252
Bubeck, S., Cesa‐Bianchi, N., & Lugosi, G. (2013) · IEEE Transactions on Information Theory · doi:10.1109/tit.2013.2277869 · Cited by 260
Multi-Material Decomposition Using Statistical Image Reconstruction for Spectral CT
Long, Y. Q., & Fessler, J. A. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2320284 · Cited by 252
Independent component analysis by general nonlinear Hebbian-like learning rules
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Adversarial Feature Selection Against Evasion Attacks
Zhang, F., Chan, P. P. K., Biggio, B., Yeung, D., & Roli, F. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2015.2415032 · Cited by 253
Axiomatic utility theories with the betweenness property
Chew, S. H. (1989) · Annals of Operations Research · doi:10.1007/bf02283525 · Cited by 241
Mathematical Programming for Data Mining: Formulations and Challenges
Bradley, P., Fayyad, U. M., & Mangasarian, O. L. (1999) · INFORMS journal on computing · doi:10.1287/ijoc.11.3.217 · Cited by 248
Reweighting strategies in seismic deconvolution
Sacchi, M. D. (1997) · Geophysical Journal International · doi:10.1111/j.1365-246x.1997.tb04500.x · Cited by 248
Nonlinear modelling and support vector machines
Suykens, J. A. K. (2002) · Conference proceedings - IEEE Instrumentation/Measurement Technology Conference · doi:10.1109/imtc.2001.928828 · Cited by 252
The astrometric core solution for theGaiamission
Lindegren, L., Lammers, U., Hobbs, D., O’Mullane, W., Bastian, U., & Hernández, J. (2011) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201117905 · Cited by 259
Pericoli, M., & Sbracia, M. (2009) · SSRN Electronic Journal · doi:10.2139/ssrn.1387462 · Cited by 245
Improved batch correction in untargeted MS-based metabolomics
Wehrens, R., Hageman, J. A., van Eeuwijk, F., Kooke, R., Flood, P. J., Wijnker, T. G., Keurentjes, J. J. B., Lommen, A., van Eekelen, H. D. L. M., Hall, R. D., et al. (2016) · Metabolomics · doi:10.1007/s11306-016-1015-8 · Cited by 248
Identification of Sources of Phoenix Aerosol by Positive Matrix Factorization
Ramadan, Z., Song, X., & Hopke, P. K. (2000) · Journal of the Air & Waste Management Association · doi:10.1080/10473289.2000.10464173 · Cited by 244
Model selection approaches for non-linear system identification: a review
Hong, X., Mitchell, R., Chen, S., Harris, C. J., Li, K., & Irwin, G. W. (2008) · International Journal of Systems Science · doi:10.1080/00207720802083018 · Cited by 244
High-Breakdown Robust Multivariate Methods
Hubert, M., Rousseeuw, P. J., & Aelst, S. V. (2008) · Statistical Science · doi:10.1214/088342307000000087 · Cited by 243
Zhao, J., & Mili, L. (2017) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2017.2761452 · Cited by 238
Lavallée, S., & Szeliski, R. (1995) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.385980 · Cited by 243
Stereo Matching with Nonlinear Diffusion
Scharstein, D., & Szeliski, R. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008015117424 · Cited by 242
Wōtan: Comprehensive Time-series Detrending in Python
Hippke, M., David, T. J., Mulders, G. D., & Heller, R. (2019) · The Astronomical Journal · doi:10.3847/1538-3881/ab3984 · Cited by 249
Objective functions for training new hidden units in constructive neural networks
Kwok, T.-Y., & Yeung, D. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/72.623214 · Cited by 237
Boos, D. D. (1992) · The American Statistician · doi:10.1080/00031305.1992.10475921 · Cited by 241
An EM-like algorithm for color-histogram-based object tracking
Živković, Z., & Kröse, B. (2004) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2004.1315113 · Cited by 241
Rejecting Mismatches by Correspondence Function
Li, X., & Hu, Z. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-010-0318-x · Cited by 240
A New Regression Model: Modal Linear Regression
Yao, W., & Li, L. (2013) · Scandinavian Journal of Statistics · doi:10.1111/sjos.12054 · Cited by 242
Conjugate-gradient preconditioning methods for shift-variant PET image reconstruction
Fessler, J. A., & Booth, S. D. (1999) · IEEE Transactions on Image Processing · doi:10.1109/83.760336 · Cited by 237
Segmentation of microcalcifications in mammograms
Dengler, J., Behrens, S., & Desaga, J. F. (1993) · IEEE Transactions on Medical Imaging · doi:10.1109/42.251111 · Cited by 237
Péng, S. (2009) · Science in China Series A Mathematics · doi:10.1007/s11425-009-0121-8 · Cited by 234
Ramani, S., Liu, Z., Rosen, J., Nielsen, J., & Fessler, J. A. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2195015 · Cited by 231
Practical Issues in Forecasting Volatility
Poon, S., & Granger, C. W. J. (2005) · Financial Analysts Journal · doi:10.2469/faj.v61.n1.2683 · Cited by 228
Advanced Impulse Detection Based on Pixel-Wise MAD
Crnojevic, V. S., Šenk, V., & Trpovski, Ž. (2004) · IEEE Signal Processing Letters · doi:10.1109/lsp.2004.830117 · Cited by 232
Regularized Robust Coding for Face Recognition
Yang, M., Lei Zhang, A., Yang, J. J., & Zhang, D. D. (2013) · Cited by 225
Economic Distance and Cross-Country Spillovers
Conley, T. G., & Ligon, E. A. (2002) · Journal of Economic Growth · doi:10.1023/a:1015676113101 · Cited by 232
MEASUREMENT ERROR IN THE BANK OF ITALY'S SURVEY OF HOUSEHOLD INCOME AND WEALTH
Biancotti, C., D’Alessio, G., & Neri, A. (2008) · Review of Income and Wealth · doi:10.1111/j.1475-4991.2008.00283.x · Cited by 229
Robust computation of optical flow in a multi-scale differential framework
Weber, J. W., & Malik, J. (1995) · International Journal of Computer Vision · doi:10.1007/bf01421489 · Cited by 233
Huber-Based Divided Difference Filtering
Karlgaard, C. D., & Schaub, H. (2007) · Journal of Guidance Control and Dynamics · doi:10.2514/1.27968 · Cited by 227
A review of robust clustering methods
García-Escudero, L. Á., Gordaliza, A., Matrán, C., & Mayo-Íscar, A. (2010) · Advances in Data Analysis and Classification · doi:10.1007/s11634-010-0064-5 · Cited by 221
Variational fluid flow measurements from image sequences: synopsis and perspectives
Heitz, D., Mémin, É., & Schnörr, C. (2009) · Experiments in Fluids · doi:10.1007/s00348-009-0778-3 · Cited by 227
Zhang, Z., Waldman, D. A., & Wang, Z. (2012) · Personnel Psychology · doi:10.1111/j.1744-6570.2011.01238.x · Cited by 237
Stability of phase information
Fleet, D. J., & Jepson, A. D. (1993) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.250844 · Cited by 222
Optimizing the noise versus bias trade-off for Illumina Whole Genome Expression Beadchips
Shi, W., Oshlack, A., & Smyth, G. K. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq871 · Cited by 225
REML estimation: asymptotic behavior and related topics
Jiang, J. (1996) · The Annals of Statistics · doi:10.1214/aos/1033066209 · Cited by 217
A Globally Optimal Algorithm for Robust TV-L1 Range Image Integration
Zach, C., Pock, T., & Bischof, H. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408983 · Cited by 220
McNicholas, P. D. (2016) · Journal of Classification · doi:10.1007/s00357-016-9211-9 · Cited by 223
Jackknife-After-Bootstrap Standard Errors and Influence Functions
Efron, B. (1992) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1992.tb01866.x · Cited by 223
Evaluation of the smoke-injection height from wild-land fires using remote-sensing data
Sofiev, M. A., Ermakova, T. S., & Vankevich, R. (2012) · Atmospheric chemistry and physics · doi:10.5194/acp-12-1995-2012 · Cited by 220
Robust Likelihood Methods Based on the Skew‐t and Related Distributions
Azzalini, A., & Genton, M. G. (2007) · International Statistical Review · doi:10.1111/j.1751-5823.2007.00016.x · Cited by 220
Review of Classifier Combination Methods
Tulyakov, S., Jaeger, S., Govindaraju, V., & Doermann, D. (2007) · Studies in computational intelligence · doi:10.1007/978-3-540-76280-5_14 · Cited by 227
Slope heuristics: overview and implementation
Baudry, J.-P., Maugis-Rabusseau, C., & Michel, B. (2011) · Statistics and Computing · doi:10.1007/s11222-011-9236-1 · Cited by 248
Application of Coherent Risk Measures to Capital Requirements in Insurance
Artzner, P. (1999) · North American Actuarial Journal · doi:10.1080/10920277.1999.10595795 · Cited by 214
Robust principal component analysis by self-organizing rules based on statistical physics approach
Xu, L., & Yuille, A. (1995) · IEEE Transactions on Neural Networks · doi:10.1109/72.363442 · Cited by 215
Statistical approaches in quantitative positron emission tomography
Leahy, R. M., & Qi, J. (2000) · Statistics and Computing · doi:10.1023/a:1008946426658 · Cited by 215
Highly Robust Variogram Estimation
Genton, M. G. (1998) · Mathematical Geology · doi:10.1023/a:1021728614555 · Cited by 216
Robustness and Pricing with Uncertain Growth
Cagetti, M., Hansen, L. P., Sargent, T. J., et al. (2002) · Review of Financial Studies · doi:10.1093/rfs/15.2.363 · Cited by 215
Penning Trap Measurements of the Masses of C133s , R87,85b , and N23a with Uncertainties ≤0.2 ppb
Bradley, M. P., Porto, J. V., Rainville, S., Thompson, J. K., & Pritchard, D. E. (1999) · Physical Review Letters · doi:10.1103/physrevlett.83.4510 · Cited by 213
Determining Microbial Kinetic Parameters Using Nonlinear Regression Analysis
Robinson, J. A. (1985) · Advances in microbial ecology · doi:10.1007/978-1-4615-9412-3_2 · Cited by 215
A similarity-based robust clustering method
Yang, M., & Wu, K.-L. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.1265860 · Cited by 214
Özyurt, D. B., & Pike, R. W. (2003) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2003.07.001 · Cited by 214
Multiregion Image Segmentation by Parametric Kernel Graph Cuts
Salah, M. B., Mitiche, A., & Ayed, I. B. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2066982 · Cited by 213
A Route Map for Successful Applications of Geographically Weighted Regression
Comber, A., Brunsdon, C., Charlton, M. E., Dong, G., Harris, R., Lu, B., Lü, Y., Murakami, D., Nakaya, T., Wang, Y., et al. (2022) · Geographical Analysis · doi:10.1111/gean.12316 · Cited by 228
A New Robust Variable Step-Size NLMS Algorithm
Vega, L. J. R., Rey, H. G., Benesty, J., et al. (2008) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2007.913142 · Cited by 213
Routes to improving the reliability of low level DNA analysis using real-time PCR
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Public administration and politics meet turbulence: The search for robust governance responses
Ansell, C., Sørensen, E., & Torfing, J. (2022) · Public Administration · doi:10.1111/padm.12874 · Cited by 295
Detecting small, moving objects in image sequences using sequential hypothesis testing
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Feature Selection via Mathematical Programming
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Econometric Evaluation of Asset Pricing Models
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ROBUSTNESS OF THE MULTI‐OBJECTIVE MOORA METHOD WITH A TEST FOR THE FACILITIES SECTOR
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A robust approach to image enhancement based on fuzzy logic
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Earthquake locations in southern California obtained using source‐specific station terms
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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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