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Journal articles on the topic 'Iteratively reweighted least squares'

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Published: 4 June 2021
Last updated: 5 February 2022

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1

Chen, Colin. "Distributed iteratively reweighted least squares and applications." Statistics and Its Interface 6, no. 4 (2013): 585–93. http://dx.doi.org/10.4310/sii.2013.v6.n4.a15.

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2

O’Leary, Dianne P. "Robust Regression Computation Using Iteratively Reweighted Least Squares." SIAM Journal on Matrix Analysis and Applications 11, no. 3 (July 1990): 466–80. http://dx.doi.org/10.1137/0611032.

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3

Ba, Demba, Behtash Babadi, Patrick L. Purdon, and Emery N. Brown. "Robust spectrotemporal decomposition by iteratively reweighted least squares." Proceedings of the National Academy of Sciences 111, no. 50 (December 2, 2014): E5336—E5345. http://dx.doi.org/10.1073/pnas.1320637111.

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4

Dollinger, Michael B., and Robert G. Staudte. "Influence Functions of Iteratively Reweighted Least Squares Estimators." Journal of the American Statistical Association 86, no. 415 (September 1991): 709–16. http://dx.doi.org/10.1080/01621459.1991.10475099.

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5

Daubechies, Ingrid, Ronald DeVore, Massimo Fornasier, and C. Si̇nan Güntürk. "Iteratively reweighted least squares minimization for sparse recovery." Communications on Pure and Applied Mathematics 63, no. 1 (January 2010): 1–38. http://dx.doi.org/10.1002/cpa.20303.

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6

Sigl, Juliane. "Nonlinear residual minimization by iteratively reweighted least squares." Computational Optimization and Applications 64, no. 3 (February 2, 2016): 755–92. http://dx.doi.org/10.1007/s10589-016-9829-x.

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7

Merli, Marcello, and Luciana Sciascia. "Iteratively reweighted least squares in crystal structure refinements." Acta Crystallographica Section A Foundations of Crystallography 67, no. 5 (July 20, 2011): 456–68. http://dx.doi.org/10.1107/s0108767311023622.

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8

Guo, Jianfeng. "Analytical quality assessment of iteratively reweighted least-squares (IRLS) method." Boletim de Ciências Geodésicas 20, no. 1 (March 2014): 132–41. http://dx.doi.org/10.1590/s1982-21702014000100009.

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The iteratively reweighted least-squares (IRLS) technique has been widely employed in geodetic and geophysical literature. The reliability measures are important diagnostic tools for inferring the strength of the model validation. An exact analytical method is adopted to obtain insights on how much iterative reweighting can affect the quality indicators. Theoretical analyses and numerical results show that, when the downweighting procedure is performed, (1) the precision, all kinds of dilution of precision (DOP) metrics and the minimal detectable bias (MDB) will become larger; (2) the variations of the bias-to-noise ratio (BNR) are involved, and (3) all these results coincide with those obtained by the first-order approximation method.
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9

Zhang, Zhi-Min, Shan Chen, and Yi-Zeng Liang. "Baseline correction using adaptive iteratively reweighted penalized least squares." Analyst 135, no. 5 (2010): 1138. http://dx.doi.org/10.1039/b922045c.

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10

Pires, R. C., A. Simoes Costa, and L. Mili. "Iteratively reweighted least-squares state estimation through Givens Rotations." IEEE Transactions on Power Systems 14, no. 4 (1999): 1499–507. http://dx.doi.org/10.1109/59.801941.

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11

Lai, Ming-Jun, Yangyang Xu, and Wotao Yin. "Improved Iteratively Reweighted Least Squares for Unconstrained Smoothed $\ell_q$ Minimization." SIAM Journal on Numerical Analysis 51, no. 2 (January 2013): 927–57. http://dx.doi.org/10.1137/110840364.

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12

Wolke, R., and H. Schwetlick. "Iteratively Reweighted Least Squares: Algorithms, Convergence Analysis, and Numerical Comparisons." SIAM Journal on Scientific and Statistical Computing 9, no. 5 (September 1988): 907–21. http://dx.doi.org/10.1137/0909062.

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13

Marx, Brian D. "Iteratively Reweighted Partial Least Squares Estimation for Generalized Linear Regression." Technometrics 38, no. 4 (November 1996): 374–81. http://dx.doi.org/10.1080/00401706.1996.10484549.

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14

Fornasier, Massimo, Holger Rauhut, and Rachel Ward. "Low-rank Matrix Recovery via Iteratively Reweighted Least Squares Minimization." SIAM Journal on Optimization 21, no. 4 (October 2011): 1614–40. http://dx.doi.org/10.1137/100811404.

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15

Bergström, Per, and Ove Edlund. "Robust registration of point sets using iteratively reweighted least squares." Computational Optimization and Applications 58, no. 3 (February 21, 2014): 543–61. http://dx.doi.org/10.1007/s10589-014-9643-2.

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16

Fusiello, Andrea, and Fabio Crosilla. "FAST AND RESISTANT PROCRUSTEAN BUNDLE ADJUSTMENT." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 3, 2016): 35–41. http://dx.doi.org/10.5194/isprsannals-iii-3-35-2016.

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In a recent paper (Fusiello and Crosilla, 2015) a Procrustean formulation of the bundle block adjustment has been presented, with a solution based on alternating least squares. This paper improves on it in two respects: it introduces a faster iterative scheme that minimizes the same cost function, thereby achieving the same accuracy, and makes the method resistant to rogue measures through iteratively reweighted least-squares. Empirical results confirm the effectiveness of these enhancements.
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17

Fusiello, Andrea, and Fabio Crosilla. "FAST AND RESISTANT PROCRUSTEAN BUNDLE ADJUSTMENT." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 3, 2016): 35–41. http://dx.doi.org/10.5194/isprs-annals-iii-3-35-2016.

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In a recent paper (Fusiello and Crosilla, 2015) a Procrustean formulation of the bundle block adjustment has been presented, with a solution based on alternating least squares. This paper improves on it in two respects: it introduces a faster iterative scheme that minimizes the same cost function, thereby achieving the same accuracy, and makes the method resistant to rogue measures through iteratively reweighted least-squares. Empirical results confirm the effectiveness of these enhancements.
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18

Jabr, R. A., and B. C. Pal. "Iteratively reweighted least-squares implementation of the WLAV state-estimation method." IEE Proceedings - Generation, Transmission and Distribution 151, no. 1 (2004): 103. http://dx.doi.org/10.1049/ip-gtd:20040030.

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19

Chen, Chen, Lei He, Hongsheng Li, and Junzhou Huang. "Fast iteratively reweighted least squares algorithms for analysis-based sparse reconstruction." Medical Image Analysis 49 (October 2018): 141–52. http://dx.doi.org/10.1016/j.media.2018.08.002.

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20

Giampouras, Paris V., Athanasios A. Rontogiannis, and Konstantinos D. Koutroumbas. "Alternating Iteratively Reweighted Least Squares Minimization for Low-Rank Matrix Factorization." IEEE Transactions on Signal Processing 67, no. 2 (January 15, 2019): 490–503. http://dx.doi.org/10.1109/tsp.2018.2883921.

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21

Miosso, C. J., R. von Borries, M. Argaez, L. Velazquez, C. Quintero, and C. M. Potes. "Compressive Sensing Reconstruction With Prior Information by Iteratively Reweighted Least-Squares." IEEE Transactions on Signal Processing 57, no. 6 (June 2009): 2424–31. http://dx.doi.org/10.1109/tsp.2009.2016889.

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22

Heiberger, Richard M., and Richard A. Becker. "Design of anSFunction for Robust Regression Using Iteratively Reweighted Least Squares." Journal of Computational and Graphical Statistics 1, no. 3 (September 1992): 181–96. http://dx.doi.org/10.1080/10618600.1992.10474580.

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23

Gao, Zhenglei, John W. Green, Jan Vanderborght, and Walter Schmitt. "Improving uncertainty analysis in kinetic evaluations using iteratively reweighted least squares." Environmental Toxicology and Chemistry 30, no. 10 (August 24, 2011): 2363–71. http://dx.doi.org/10.1002/etc.630.

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24

Yuan, Ke-Hai, and Peter M. Bentler. "Robust mean and covariance structure analysis through iteratively reweighted least squares." Psychometrika 65, no. 1 (March 2000): 43–58. http://dx.doi.org/10.1007/bf02294185.

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25

Wang, Yong. "Minimum disparity computation via the iteratively reweighted least integrated squares algorithms." Computational Statistics & Data Analysis 51, no. 12 (August 2007): 5662–72. http://dx.doi.org/10.1016/j.csda.2007.05.033.

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26

Street, James O., Raymond J. Carroll, and David Ruppert. "A Note on Computing Robust Regression Estimates Via Iteratively Reweighted Least Squares." American Statistician 42, no. 2 (May 1988): 152. http://dx.doi.org/10.2307/2684491.

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27

ZHANG Yue-qiang, 张跃强, 苏昂 SU Ang, 刘海波 LIU Hai-bo, 尚洋 SHANG Yang, and 于起峰 YU Qi-feng. "Pose estimation based on multiple line hypothesis and iteratively reweighted least squares." Optics and Precision Engineering 23, no. 6 (2015): 1722–31. http://dx.doi.org/10.3788/ope.20152306.1722.

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28

Chen, Kai, Qi Lv, Yao Lu, and Yong Dou. "Robust regularized extreme learning machine for regression using iteratively reweighted least squares." Neurocomputing 230 (March 2017): 345–58. http://dx.doi.org/10.1016/j.neucom.2016.12.029.

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29

Street, James O., Raymond J. Carroll, and David Ruppert. "A Note on Computing Robust Regression Estimates via Iteratively Reweighted Least Squares." American Statistician 42, no. 2 (May 1988): 152–54. http://dx.doi.org/10.1080/00031305.1988.10475548.

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30

Cummins, David J., and C. Webster Andrews. "Iteratively reweighted partial least squares: A performance analysis by monte carlo simulation." Journal of Chemometrics 9, no. 6 (November 1995): 489–507. http://dx.doi.org/10.1002/cem.1180090607.

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31

Stone, Richard E., and Craig A. Tovey. "The Simplex and Projective Scaling Algorithms as Iteratively Reweighted Least Squares Methods." SIAM Review 33, no. 2 (June 1991): 220–37. http://dx.doi.org/10.1137/1033049.

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32

Marx, Christian. "On resistant Lp-Norm Estimation by means of iteratively reweighted least Squares." Journal of Applied Geodesy 7, no. 1 (January 2013): 1–10. http://dx.doi.org/10.1515/jag-2012-0042.

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33

Seo, Suyoung. "Image Denoising and Refinement Based on an Iteratively Reweighted Least Squares Filter." KSCE Journal of Civil Engineering 24, no. 3 (February 7, 2020): 943–53. http://dx.doi.org/10.1007/s12205-020-2103-x.

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34

Correia, Sergio, Paulo Guimarães, and Tom Zylkin. "Fast Poisson estimation with high-dimensional fixed effects." Stata Journal: Promoting communications on statistics and Stata 20, no. 1 (March 2020): 95–115. http://dx.doi.org/10.1177/1536867x20909691.

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In this article, we present ppmlhdfe, a new command for estimation of (pseudo-)Poisson regression models with multiple high-dimensional fixed effects (HDFE). Estimation is implemented using a modified version of the iteratively reweighted least-squares algorithm that allows for fast estimation in the presence of HDFE. Because the code is built around the reghdfe package ( Correia, 2014 , Statistical Software Components S457874, Department of Economics, Boston College), it has similar syntax, supports many of the same functionalities, and benefits from reghdfe‘s fast convergence properties for computing high-dimensional leastsquares problems. Performance is further enhanced by some new techniques we introduce for accelerating HDFE iteratively reweighted least-squares estimation specifically. ppmlhdfe also implements a novel and more robust approach to check for the existence of (pseudo)maximum likelihood estimates.
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35

Chakraborty, Madhuparna, Alaka Barik, Ravinder Nath, and Victor Dutta. "NonConvex Iteratively Reweighted Least Square Optimization in Compressive Sensing." Advanced Materials Research 341-342 (September 2011): 629–33. http://dx.doi.org/10.4028/www.scientific.net/amr.341-342.629.

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In this paper, we study a method for sparse signal recovery with the help of iteratively reweighted least square approach, which in many situations outperforms other reconstruction method mentioned in literature in a way that comparatively fewer measurements are needed for exact recovery. The algorithm given involves solving a sequence of weighted minimization for nonconvex problems where the weights for the next iteration are determined from the value of current solution. We present a number of experiments demonstrating the performance of the algorithm. The performance of the algorithm is studied via computer simulation for different number of measurements, and degree of sparsity. Also the simulation results show that improvement is achieved by incorporating regularization strategy.
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36

Burrus, C. S., J. A. Barreto, and I. W. Selesnick. "Iterative reweighted least-squares design of FIR filters." IEEE Transactions on Signal Processing 42, no. 11 (1994): 2926–36. http://dx.doi.org/10.1109/78.330353.

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37

Tong, H. B., G. Z. Zhang, and G. Ou. "Iterative reweighted recursive least squares for robust positioning." Electronics Letters 48, no. 13 (2012): 789. http://dx.doi.org/10.1049/el.2012.0546.

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38

Heiberger, Richard M., and Richard A. Becker. "Design of an S Function for Robust Regression Using Iteratively Reweighted Least Squares." Journal of Computational and Graphical Statistics 1, no. 3 (September 1992): 181. http://dx.doi.org/10.2307/1390715.

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39

Luo, Xian, and Wanzhou Ye. "Continuous Iteratively Reweighted Least Squares Algorithm for Solving Linear Models by Convex Relaxation." Advances in Pure Mathematics 09, no. 06 (2019): 523–33. http://dx.doi.org/10.4236/apm.2019.96024.

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40

Canyi Lu, Zhouchen Lin, and Shuicheng Yan. "Smoothed Low Rank and Sparse Matrix Recovery by Iteratively Reweighted Least Squares Minimization." IEEE Transactions on Image Processing 24, no. 2 (February 2015): 646–54. http://dx.doi.org/10.1109/tip.2014.2380155.

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41

Cai, Yun, and Song Li. "Convergence and stability of iteratively reweighted least squares for low-rank matrix recovery." Inverse Problems & Imaging 11, no. 4 (2017): 643–61. http://dx.doi.org/10.3934/ipi.2017030.

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42

Stone, Richard E., and Craig A. Tovey. "Erratum: The Simplex and Projective Scaling Algorithms as Iteratively Reweighted Least Squares Methods." SIAM Review 33, no. 3 (September 1991): 461. http://dx.doi.org/10.1137/1033100.

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43

Bissantz, Nicolai, Lutz Dümbgen, Axel Munk, and Bernd Stratmann. "Convergence Analysis of Generalized Iteratively Reweighted Least Squares Algorithms on Convex Function Spaces." SIAM Journal on Optimization 19, no. 4 (January 2009): 1828–45. http://dx.doi.org/10.1137/050639132.

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44

Mbamalu, G. A. N., and M. E. El-Hawary. "Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation." IEEE Transactions on Power Systems 8, no. 1 (1993): 343–48. http://dx.doi.org/10.1109/59.221222.

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45

El-Hawary, M. E., and G. A. N. Mbamalu. "Short-term power system load forecasting using the iteratively reweighted least squares algorithm." Electric Power Systems Research 19, no. 1 (July 1990): 11–22. http://dx.doi.org/10.1016/0378-7796(90)90003-l.

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46

Liu, Hai, Luxin Yan, Tao Huang, Sanya Liu, and Zhaoli Zhang. "Blind Spectral Signal Deconvolution with Sparsity Regularization: An Iteratively Reweighted Least-Squares Solution." Circuits, Systems, and Signal Processing 36, no. 1 (April 22, 2016): 435–46. http://dx.doi.org/10.1007/s00034-016-0318-3.

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47

Nisa, Khoirin, and Netti Herawati. "Robust Estimation of Generalized Estimating Equation when Data Contain Outliers." INSIST 2, no. 1 (March 22, 2017): 1. http://dx.doi.org/10.23960/ins.v2i1.23.

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Abstract—In this paper, a robust procedure for estimating parameters of regression model when generalized estimating equation (GEE) applied to longitudinal data that contains outliers is proposed. The method is called ‘iteratively reweighted least trimmed square’ (IRLTS) which is a combination of the iteratively reweighted least square (IRLS) and least trimmed square (LTS) methods. To assess the proposed method a simulation study was conducted and the result shows that the method is robust against outliers.Keywords—GEE, IRLS, LTS, longitudinal data, regression model.
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48

Li, Chuang, Jianping Huang, Zhenchun Li, Han Yu, and Rongrong Wang. "Least-squares migration with primary- and multiple-guided weighting matrices." GEOPHYSICS 84, no. 3 (May 1, 2019): S171—S185. http://dx.doi.org/10.1190/geo2018-0038.1.

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Least-squares migration (LSM) of seismic data is supposed to produce images of subsurface structures with better quality than standard migration if we have an accurate migration velocity model. However, LSM suffers from data mismatch problems and migration artifacts when noise pollutes the recorded profiles. This study has developed a reweighted least-squares reverse time migration (RWLSRTM) method to overcome the problems caused by such noise. We first verify that spiky noise and free-surface multiples lead to the mismatch problems and should be eliminated from the data residual. The primary- and multiple-guided weighting matrices are then derived for RWLSRTM to reduce the noise in the data residual. The weighting matrices impose constraints on the data residual such that spiky noise and free-surface multiple reflections are reduced whereas primary reflections are preserved. The weights for spiky noise and multiple reflections are controlled by a dynamic threshold parameter decreasing with iterations for better results. Finally, we use an iteratively reweighted least-squares algorithm to minimize the weighted data residual. We conduct numerical tests using the synthetic data and compared the results of this method with the results of standard LSRTM. The results suggest that RWLSRTM is more robust than standard LSRTM when the seismic data contain spiky noise and multiple reflections. Moreover, our method not only suppresses the migration artifacts, but it also accelerates the convergence.
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49

ZHANG Zhen-jie, 张振杰, 郝向阳 HAO Xiang-yang, 程传奇 CHENG Chuan-qi, and 黄忠义 HUANG Zhong-yi. "Iteratively reweighted least squares method for camera pose estimation based on coplanar line correspondences." Optics and Precision Engineering 24, no. 5 (2016): 1168–75. http://dx.doi.org/10.3788/ope.20162405.1168.

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50

Wolke, Ralf. "Iteratively reweighted least squares: A comparison of several single step algorithms for linear models." BIT 32, no. 3 (September 1992): 506–24. http://dx.doi.org/10.1007/bf02074884.

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