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Journal articles on the topic 'Penalized least squares'

Author: Grafiati

Published: 4 June 2021

Last updated: 28 January 2023

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1

Kaufman, L. "Maximum likelihood, least squares, and penalized least squares for PET." IEEE Transactions on Medical Imaging 12, no. 2 (June 1993): 200–214. http://dx.doi.org/10.1109/42.232249.

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2

Eubank, R. L., and R. F. Gunst. "Diagnostics for penalized least-squares estimators." Statistics & Probability Letters 4, no. 5 (August 1986): 265–72. http://dx.doi.org/10.1016/0167-7152(86)90101-x.

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3

Wibowo, Wahyu, Sri Haryatmi, and I. Nyoman Budiantara. "Penalized least squares for semiparametric regression." International Journal of Academic Research 4, no. 6 (November 9, 2012): 281–86. http://dx.doi.org/10.7813/2075-4124.2012/4-6/a.39.

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4

Kohler, M., and A. Krzyzak. "Nonparametric regression estimation using penalized least squares." IEEE Transactions on Information Theory 47, no. 7 (2001): 3054–58. http://dx.doi.org/10.1109/18.998089.

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5

Bates, Douglas M., and Saikat DebRoy. "Linear mixed models and penalized least squares." Journal of Multivariate Analysis 91, no. 1 (October 2004): 1–17. http://dx.doi.org/10.1016/j.jmva.2004.04.013.

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6

Guerrero, Victor M. "Time series smoothing by penalized least squares." Statistics & Probability Letters 77, no. 12 (July 2007): 1225–34. http://dx.doi.org/10.1016/j.spl.2007.03.006.

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7

Peng, Heng, and Tao Huang. "Penalized least squares for single index models." Journal of Statistical Planning and Inference 141, no. 4 (April 2011): 1362–79. http://dx.doi.org/10.1016/j.jspi.2010.10.003.

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8

Wittich, O., A. Kempe, G. Winkler, and V. Liebscher. "Complexity penalized least squares estimators: Analytical results." Mathematische Nachrichten 281, no. 4 (April 2008): 582–95. http://dx.doi.org/10.1002/mana.200510627.

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9

Spiriti, Steven, Randall Eubank, Philip W. Smith, and Dennis Young. "Knot selection for least-squares and penalized splines." Journal of Statistical Computation and Simulation 83, no. 6 (June 2013): 1020–36. http://dx.doi.org/10.1080/00949655.2011.647317.

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10

Zhu, Rong, Guohua Zou, Hua Liang, and Lixing Zhu. "Penalized Weighted Least Squares to Small Area Estimation." Scandinavian Journal of Statistics 43, no. 3 (December 18, 2015): 736–56. http://dx.doi.org/10.1111/sjos.12201.

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11

Shijun, Ding, and Tao Benzao. "Generalized penalized least squares and its statistical characteristics." Geo-spatial Information Science 9, no. 4 (January 2006): 255–59. http://dx.doi.org/10.1007/bf02826736.

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12

Fan, JianQing, Lei Qi, and Xin Tong. "Penalized least squares estimation with weakly dependent data." Science China Mathematics 59, no. 12 (November 16, 2016): 2335–54. http://dx.doi.org/10.1007/s11425-016-0098-x.

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13

Muro, Alan, and Sara van de Geer. "Concentration behavior of the penalized least squares estimator." Statistica Neerlandica 72, no. 2 (February 26, 2018): 109–25. http://dx.doi.org/10.1111/stan.12123.

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14

Li, Zhong, De-Jian Zhan, Jia-Jun Wang, Jing Huang, Qing-Song Xu, Zhi-Min Zhang, Yi-Bao Zheng, Yi-Zeng Liang, and Hong Wang. "Morphological weighted penalized least squares for background correction." Analyst 138, no. 16 (2013): 4483. http://dx.doi.org/10.1039/c3an00743j.

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15

Androulakis, E., C. Koukouvinos, and K. Mylona. "Tuning Parameter Estimation in Penalized Least Squares Methodology." Communications in Statistics - Simulation and Computation 40, no. 9 (October 2011): 1444–57. http://dx.doi.org/10.1080/03610918.2011.575507.

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16

Guyon, Xavier, and Cécile Hardouin. "Misparametrization subsets for penalized least squares model selection." Statistical Inference for Stochastic Processes 17, no. 3 (June 13, 2014): 283–94. http://dx.doi.org/10.1007/s11203-014-9100-y.

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17

Baek, Sung-June, Aaron Park, Young-Jin Ahn, and Jaebum Choo. "Baseline correction using asymmetrically reweighted penalized least squares smoothing." Analyst 140, no. 1 (2015): 250–57. http://dx.doi.org/10.1039/c4an01061b.

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18

Zhang, Feng, Xiaojun Tang, Angxin Tong, Bin Wang, and Jingwei Wang. "An Automatic Baseline Correction Method Based on the Penalized Least Squares Method." Sensors 20, no. 7 (April 3, 2020): 2015. http://dx.doi.org/10.3390/s20072015.

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Baseline drift spectra are used for quantitative and qualitative analysis, which can easily lead to inaccurate or even wrong results. Although there are several baseline correction methods based on penalized least squares, they all have one or more parameters that must be optimized by users. For this purpose, an automatic baseline correction method based on penalized least squares is proposed in this paper. The algorithm first linearly expands the ends of the spectrum signal, and a Gaussian peak is added to the expanded range. Then, the whole spectrum is corrected by the adaptive smoothness parameter penalized least squares (asPLS) method, that is, by turning the smoothing parameter λ of asPLS to obtain a different root-mean-square error (RMSE) in the extended range, the optimal λ is selected with minimal RMSE. Finally, the baseline of the original signal is well estimated by asPLS with the optimal λ. The paper concludes with the experimental results on the simulated spectra and measured infrared spectra, demonstrating that the proposed method can automatically deal with different types of baseline drift.

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19

Park, Aaron, Sung-June Baek, Jun-Qyu Park, Yu-Gyung Seo, and Yonggwan Won. "Automatic Selection of Optimal Parameter for Baseline Correction using Asymmetrically Reweighted Penalized Least Squares." Journal of the Institute of Electronics and Information Engineers 53, no. 3 (March 25, 2016): 124–31. http://dx.doi.org/10.5573/ieie.2016.53.3.124.

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20

Urbas, Aaron A., and Steven J. Choquette. "Automated Spectral Smoothing with Spatially Adaptive Penalized Least Squares." Applied Spectroscopy 65, no. 6 (June 2011): 665–77. http://dx.doi.org/10.1366/10-05971.

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21

Bunea, Florentina, Yiyuan She, Hernando Ombao, Assawin Gongvatana, Kate Devlin, and Ronald Cohen. "Penalized least squares regression methods and applications to neuroimaging." NeuroImage 55, no. 4 (April 2011): 1519–27. http://dx.doi.org/10.1016/j.neuroimage.2010.12.028.

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22

Baramidze, Victoria, and Ming-Jun Lai. "Convergence of discrete and penalized least squares spherical splines." Journal of Approximation Theory 163, no. 9 (September 2011): 1091–106. http://dx.doi.org/10.1016/j.jat.2011.03.001.

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23

Forthmann, Peter, Thomas Koehler, Michel Defrise, and Patrick La Riviere. "Comparing implementations of penalized weighted least-squares sinogram restoration." Medical Physics 37, no. 11 (October 26, 2010): 5929–38. http://dx.doi.org/10.1118/1.3490476.

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24

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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25

Fort, G., and S. Lambert-Lacroix. "Classification using partial least squares with penalized logistic regression." Bioinformatics 21, no. 7 (November 5, 2004): 1104–11. http://dx.doi.org/10.1093/bioinformatics/bti114.

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26

Wang, Haonan, and Jun Zhu. "Variable selection in spatial regression via penalized least squares." Canadian Journal of Statistics 37, no. 4 (December 2009): 607–24. http://dx.doi.org/10.1002/cjs.10032.

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27

Yang, Guofeng, Jiacai Dai, Xiangjun Liu, Meng Chen, and Xiaolong Wu. "Multiple Constrained Reweighted Penalized Least Squares for Spectral Baseline Correction." Applied Spectroscopy 74, no. 12 (October 6, 2020): 1443–51. http://dx.doi.org/10.1177/0003702819885002.

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Baseline drift occurs in various measured spectra, and the existence of a baseline signal will influence qualitative and quantitative analyses. Therefore, it is necessary to perform baseline correction or background elimination before spectral analysis. In this paper, a multiple constrained asymmetric least squares method based on the penalized least squares principle is proposed for baseline correction. The method takes both baseline and peak characteristics into account. Based on the prior knowledge that the left and right boundaries of characteristic peaks should be symmetrical, additional constraints of penalized least squares are added, which ensure the symmetry of spectra. The experimental results of the proposed method on simulated spectra are compared with existing baseline correction methods to verify the accuracy and adaptability of the proposed method. The method is also successfully applied to the baseline correction of real spectra. The results show that it can be effective for estimating the baseline. In addition, this method can also be applied to the baseline correction of other similar spectral signals.

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28

Han, Tianhe Zhou and Danfu. "Hermite Scattered Data Fitting by the Penalized Least Squares Method." Journal of Computational Mathematics 27, no. 6 (June 2009): 802–11. http://dx.doi.org/10.4208//jcm.2009.09-m2540.

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29

Fessler, J. A. "Penalized weighted least-squares image reconstruction for positron emission tomography." IEEE Transactions on Medical Imaging 13, no. 2 (June 1994): 290–300. http://dx.doi.org/10.1109/42.293921.

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30

Beran, Rudolf. "Hypercube estimators: Penalized least squares, submodel selection, and numerical stability." Computational Statistics & Data Analysis 71 (March 2014): 654–66. http://dx.doi.org/10.1016/j.csda.2013.05.020.

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31

Xu, Degang, Song Liu, Yaoyi Cai, and Chunhua Yang. "Baseline correction method based on doubly reweighted penalized least squares." Applied Optics 58, no. 14 (May 9, 2019): 3913. http://dx.doi.org/10.1364/ao.58.003913.

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32

Porcher, R., and G. Thomas. "Order Determination in Nonlinear Time Series by Penalized Least-Squares." Communications in Statistics - Simulation and Computation 32, no. 4 (January 11, 2003): 1115–29. http://dx.doi.org/10.1081/sac-120023881.

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33

ZHOU, HUAXUE. "APPROXIMATION OF DISCRETE AND PENALIZED LEAST SQUARES SPLINES OVER SPHERICAL TRIANGULATIONS." Journal of Mathematical Sciences: Advances and Applications 47 (January 10, 2018): 21–33. http://dx.doi.org/10.18642/jmsaa_07100121872.

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34

Zhou, Huaxue. "APPROXIMATION OF DISCRETE AND PENALIZED LEAST SQUARES SPLINES OVER SPHERICAL TRIANGULATIONS." Journal of Mathematical Sciences: Advances and Applications 47 (November 7, 2017): 21–33. http://dx.doi.org/10.18642/jmsaa_7100121872.

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35

Nowak, R. D. "Penalized least squares estimation of Volterra filters and higher order statistics." IEEE Transactions on Signal Processing 46, no. 2 (1998): 419–28. http://dx.doi.org/10.1109/78.655426.

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36

Mai, Qing, and Xin Zhang. "An iterative penalized least squares approach to sparse canonical correlation analysis." Biometrics 75, no. 3 (April 9, 2019): 734–44. http://dx.doi.org/10.1111/biom.13043.

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37

Kheirati Roonizi, Arman, and Christian Jutten. "Forward-backward filtering and penalized least-Squares optimization: A Unified framework." Signal Processing 178 (January 2021): 107796. http://dx.doi.org/10.1016/j.sigpro.2020.107796.

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38

Suzuki, Takumi, and Nakahiro Yoshida. "Penalized least squares approximation methods and their applications to stochastic processes." Japanese Journal of Statistics and Data Science 3, no. 2 (January 1, 2020): 513–41. http://dx.doi.org/10.1007/s42081-019-00064-w.

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39

Boysen, Leif, Angela Kempe, Volkmar Liebscher, Axel Munk, and Olaf Wittich. "Consistencies and rates of convergence of jump-penalized least squares estimators." Annals of Statistics 37, no. 1 (February 2009): 157–83. http://dx.doi.org/10.1214/07-aos558.

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40

Huang, T., B. Wu, P. Lizardi, and H. Zhao. "Detection of DNA copy number alterations using penalized least squares regression." Bioinformatics 21, no. 20 (August 30, 2005): 3811–17. http://dx.doi.org/10.1093/bioinformatics/bti646.

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41

Chang, Xiangyu, Yan Zhong, Yao Wang, and Shaobo Lin. "Unified Low-Rank Matrix Estimate via Penalized Matrix Least Squares Approximation." IEEE Transactions on Neural Networks and Learning Systems 30, no. 2 (February 2019): 474–85. http://dx.doi.org/10.1109/tnnls.2018.2844242.

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42

Chen, Long, Yingwen Wu, Tianjun Li, and Zhuo Chen. "Collaborative Penalized Least Squares for Background Correction of Multiple Raman Spectra." Journal of Analytical Methods in Chemistry 2018 (August 29, 2018): 1–11. http://dx.doi.org/10.1155/2018/9031356.

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Abstract:

Although Raman spectroscopy has been widely used as a noninvasive analytical tool in various applications, backgrounds in Raman spectra impair its performance in quantitative analysis. Many algorithms have been proposed to separately correct the background spectrum by spectrum. However, in real applications, there are commonly multiple spectra collected from the close locations of a sample or from the same analyte with different concentrations. These spectra are strongly correlated and provide valuable information for more robust background correction. Herein, we propose two new strategies to remove background for a set of related spectra collaboratively. Based on weighted penalized least squares, the new approaches will use the fused weights from multiple spectra or the weights from the average spectrum to estimate the background of each spectrum in the set. Background correction results from both simulated and real experimental data demonstrate that the proposed collaborative approaches outperform traditional algorithms which process spectra individually.

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43

Zhu Feng, 朱锋, 安军社 An Junshe, 施海亮 Shi Hailiang, 叶函函 Ye Hanhan, 李志伟 Li Zhiwei, 王先华 Wang Xianhua, and 熊伟 Xiong Wei. "基于低秩约束惩罚最小二乘的干涉图基线校正方法." Acta Optica Sinica 42, no. 14 (2022): 1430001. http://dx.doi.org/10.3788/aos202242.1430001.

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44

Chang, Le, Jiali Wang, and William Woodgate. "Analysing spectroscopy data using two-step group penalized partial least squares regression." Environmental and Ecological Statistics 28, no. 2 (April 19, 2021): 445–67. http://dx.doi.org/10.1007/s10651-021-00496-2.

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45

Qin, Pan, and Ryuei Nishii. "SELECTION OF ARX MODELS ESTIMATED BY THE PENALIZED WEIGHTED LEAST SQUARES METHOD." Bulletin of informatics and cybernetics 42 (December 2010): 35–43. http://dx.doi.org/10.5109/25904.

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46

Gribonval, Rémi. "Should Penalized Least Squares Regression be Interpreted as Maximum A Posteriori Estimation?" IEEE Transactions on Signal Processing 59, no. 5 (May 2011): 2405–10. http://dx.doi.org/10.1109/tsp.2011.2107908.

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47

Treister, E., and I. Yavneh. "A Multilevel Iterated-Shrinkage Approach to $l_{1}$ Penalized Least-Squares Minimization." IEEE Transactions on Signal Processing 60, no. 12 (December 2012): 6319–29. http://dx.doi.org/10.1109/tsp.2012.2218807.

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48

Chouzenoux, Emilie, and Jean-Christophe Pesquet. "A Stochastic Majorize-Minimize Subspace Algorithm for Online Penalized Least Squares Estimation." IEEE Transactions on Signal Processing 65, no. 18 (September 15, 2017): 4770–83. http://dx.doi.org/10.1109/tsp.2017.2709265.

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49

Li, Baibing, A. Julian Morris, and Elaine B. Martin. "Generalized partial least squares regression based on the penalized minimum norm projection." Chemometrics and Intelligent Laboratory Systems 72, no. 1 (June 2004): 21–26. http://dx.doi.org/10.1016/j.chemolab.2004.01.026.

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50

Shen, Yijiang, Edmund Y. Lam, and Ngai Wong. "A Signomial Programming Approach for Binary Image Restoration by Penalized Least Squares." IEEE Transactions on Circuits and Systems II: Express Briefs 55, no. 1 (January 2008): 41–45. http://dx.doi.org/10.1109/tcsii.2007.907751.

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