Relevant bibliographies by topics / Robust regression

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Robust regression'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Robust regression"

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Sabzekar, Mostafa, and Seyed Mohammad Hossein Hasheminejad. "Robust regression using support vector regressions." Chaos, Solitons & Fractals 144 (March 2021): 110738. http://dx.doi.org/10.1016/j.chaos.2021.110738.

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Samaniego, Angel. "CAPM-alpha estimation with robust regression vs. linear regression." Análisis Económico 38, no. 97 (2023): 27–37. http://dx.doi.org/10.24275/uam/azc/dcsh/ae/2022v38n97/samaniego.

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Huang, Dong, Ricardo Cabral, and Fernando Dela Torre. "Robust Regression." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 2 (2016): 363–75. http://dx.doi.org/10.1109/tpami.2015.2448091.

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Fearn, Tom. "Robust Regression." NIR news 26, no. 1 (2015): 25–26. http://dx.doi.org/10.1255/nirn.1507.

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Maronna, Ricardo, and Stephan Morgenthaler. "Robust regression through robust covariances." Communications in Statistics - Theory and Methods 15, no. 4 (1986): 1347–65. http://dx.doi.org/10.1080/03610928608829187.

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Scott, David, and Zhipeng Wang. "Robust Multiple Regression." Entropy 23, no. 1 (2021): 88. http://dx.doi.org/10.3390/e23010088.

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As modern data analysis pushes the boundaries of classical statistics, it is timely to reexamine alternate approaches to dealing with outliers in multiple regression. As sample sizes and the number of predictors increase, interactive methodology becomes less effective. Likewise, with limited understanding of the underlying contamination process, diagnostics are likely to fail as well. In this article, we advocate for a non-likelihood procedure that attempts to quantify the fraction of bad data as a part of the estimation step. These ideas also allow for the selection of important predictors un
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TAKEZAWA, Kunio. "Robust Possibility Regression." Journal of Japan Society for Fuzzy Theory and Systems 7, no. 1 (1995): 205–9. http://dx.doi.org/10.3156/jfuzzy.7.1_205.

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Scott, David W., and Zhipeng Wang. "Robust Multiple Regression." Entropy 23, no. 1 (2021): 88. http://dx.doi.org/10.3390/e23010088.

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As modern data analysis pushes the boundaries of classical statistics, it is timely to reexamine alternate approaches to dealing with outliers in multiple regression. As sample sizes and the number of predictors increase, interactive methodology becomes less effective. Likewise, with limited understanding of the underlying contamination process, diagnostics are likely to fail as well. In this article, we advocate for a non-likelihood procedure that attempts to quantify the fraction of bad data as a part of the estimation step. These ideas also allow for the selection of important predictors un
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Shin, Ha-Young, and Hee-Seok Oh. "Robust Geodesic Regression." International Journal of Computer Vision 130, no. 2 (2022): 478–503. http://dx.doi.org/10.1007/s11263-021-01561-w.

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Rousseeuw, Peter J., Stefan Van Aelst, Katrien Van Driessen, and Jose A. Gulló. "Robust Multivariate Regression." Technometrics 46, no. 3 (2004): 293–305. http://dx.doi.org/10.1198/004017004000000329.

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Dissertations / Theses on the topic "Robust regression"

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Bai, Xue. "Robust linear regression." Kansas State University, 2012. http://hdl.handle.net/2097/14977.

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Master of Science<br>Department of Statistics<br>Weixin Yao<br>In practice, when applying a statistical method it often occurs that some observations deviate from the usual model assumptions. Least-squares (LS) estimators are very sensitive to outliers. Even one single atypical value may have a large effect on the regression parameter estimates. The goal of robust regression is to develop methods that are resistant to the possibility that one or several unknown outliers may occur anywhere in the data. In this paper, we review various robust regression methods including: M-estimate, LMS estimat
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Nottingham, Quinton J. "Model-robust quantal regression." Diss., Virginia Tech, 1995. http://hdl.handle.net/10919/40225.

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Robinson, Timothy J. "Dual Model Robust Regression." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/11244.

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In typical normal theory regression, the assumption of homogeneity of variances is often not appropriate. Instead of treating the variances as a nuisance and transforming away the heterogeneity, the structure of the variances may be of interest and it is desirable to model the variances. Aitkin (1987) proposes a parametric dual model in which a log linear dependence of the variances on a set of explanatory variables is assumed. Aitkin's parametric approach is an iterative one providing estimates for the parameters in the mean and variance models through joint maximum likelihood. Est
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Nargis, Suraiya, and n/a. "Robust methods in logistic regression." University of Canberra. Information Sciences & Engineering, 2005. http://erl.canberra.edu.au./public/adt-AUC20051111.141200.

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My Masters research aims to deepen our understanding of the behaviour of robust methods in logistic regression. Logistic regression is a special case of Generalized Linear Modelling (GLM), which is a powerful and popular technique for modelling a large variety of data. Robust methods are useful in reducing the effect of outlying values in the response variable on parameter estimates. A literature survey shows that we are still at the beginning of being able to detect extreme observations in logistic regression analyses, to apply robust methods in logistic regression and to present informativel
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Bai, Xiuqin. "Robust mixtures of regression models." Diss., Kansas State University, 2014. http://hdl.handle.net/2097/18683.

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Doctor of Philosophy<br>Department of Statistics<br>Kun Chen and Weixin Yao<br>This proposal contains two projects that are related to robust mixture models. In the robust project, we propose a new robust mixture of regression models (Bai et al., 2012). The existing methods for tting mixture regression models assume a normal distribution for error and then estimate the regression param- eters by the maximum likelihood estimate (MLE). In this project, we demonstrate that the MLE, like the least squares estimate, is sensitive to outliers and heavy-tailed error distributions. We propose a r
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Li, Xiongya. "Robust multivariate mixture regression models." Diss., Kansas State University, 2017. http://hdl.handle.net/2097/38427.

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Doctor of Philosophy<br>Department of Statistics<br>Weixing Song<br>In this dissertation, we proposed a new robust estimation procedure for two multivariate mixture regression models and applied this novel method to functional mapping of dynamic traits. In the first part, a robust estimation procedure for the mixture of classical multivariate linear regression models is discussed by assuming that the error terms follow a multivariate Laplace distribution. An EM algorithm is developed based on the fact that the multivariate Laplace distribution is a scale mixture of the multivariate standard no
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Waterman, Megan Janet Tuttle. "Linear Mixed Model Robust Regression." Diss., Virginia Tech, 2002. http://hdl.handle.net/10919/27708.

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Mixed models are powerful tools for the analysis of clustered data and many extensions of the classical linear mixed model with normally distributed response have been established. As with all parametric models, correctness of the assumed model is critical for the validity of the ensuing inference. Model robust regression techniques predict mean response as a convex combination of a parametric and a nonparametric model fit to the data. It is a semiparametric method by which incompletely or incorrectly specified parametric models can be improved through adding an appropriate amount of a nonpara
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Assaid, Christopher Ashley. "Outlier Resistant Model Robust Regression." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/30493.

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Parametric regression fitting (such as OLS) to a data set requires specification of an underlying model. If the specified model is different from the true model, then the parametric fit suffers to a degree that varies with the extent of model misspecification. Mays and Birch (1996) addressed this problem in the one regressor variable case with a method known as Model Robust Regression (MRR), which is a weighted average of independent parametric and nonparametric fits to the data. This paper was based on the underlying assumption of "well-behaved" (Normal) data. The method seeks to ta
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Agard, David B. "Robust inferential procedures applied to regression." Diss., This resource online, 1990. http://scholar.lib.vt.edu/theses/available/etd-10132005-152518/.

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Starnes, Brett Alden. "Asymptotic Results for Model Robust Regression." Diss., Virginia Tech, 1999. http://hdl.handle.net/10919/30244.

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Since the mid 1980's many statisticians have studied methods for combining parametric and nonparametric esimates to improve the quality of fits in a regression problem. Notably in 1987, Einsporn and Birch proposed the Model Robust Regression estimate (MRR1) in which estimates of the parametric function, f, and the nonparametric function, g, were combined in a straightforward fashion via the use of a mixing parameter, l. This technique was studied extensively at small samples and was shown to be quite effective at modeling various unusual functions. In 1995, Mays and Birch develop
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Books on the topic "Robust regression"

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Riazoshams, Hossein, Habshah Midi, and Gebrenegus Ghilagaber. Robust Nonlinear Regression. John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781119010463.

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Atkinson, Anthony, and Marco Riani. Robust Diagnostic Regression Analysis. Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1160-0.

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Andersen, Robert. Modern Methods for Robust Regression. SAGE Publications, Inc., 2008. http://dx.doi.org/10.4135/9781412985109.

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Rousseeuw, Peter J., and Annick M. Leroy. Robust Regression and Outlier Detection. John Wiley & Sons, Inc., 1987. http://dx.doi.org/10.1002/0471725382.

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Rousseeuw, Peter J., and Annick M. Leroy. Robust Regression and Outlier Detection. John Wiley & Sons, Inc., 1987. http://dx.doi.org/10.1002/0471725382.

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Rousseeuw, Peter J. Robust regression and outlier detection. Wiley, 1987.

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Andersen, Robert. Modern methods for robust regression. Sage Pub., 2006.

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D, Lawrence Kenneth, and Arthur Jeffrey L. 1952-, eds. Robust regression: Analysis and applications. M. Dekker, 1990.

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Oyet, Alwell J. Robust designs for wavelet approximations of regression models. University of Toronto, Dept. of Statistics, 1997.

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Wooldridge, Jeffrey M. A unified approach to robust, regression-based specification tests. Massachusetts Institute of Technology, 1988.

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Book chapters on the topic "Robust regression"

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Li, Guoying. "Robust Regression." In Exploring Data Tables, Trends, and Shapes. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118150702.ch8.

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Wilcox, Rand R. "Robust Regression." In Fundamentals of Modern Statistical Methods. Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3522-2_11.

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Huang, Dong, Ricardo Silveira Cabral, and Fernando De la Torre. "Robust Regression." In Computer Vision – ECCV 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33765-9_44.

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Wilcox, Rand R. "Robust Regression." In Fundamentals of Modern Statistical Methods. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-5525-8_11.

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Awange, Joseph L., Béla Paláncz, Robert H. Lewis, and Lajos Völgyesi. "Robust Regression." In Mathematical Geosciences. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-67371-4_13.

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Rieder, Helmut. "Robust Regression." In Robust Asymptotic Statistics. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4684-0624-5_7.

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Brown, Jonathon D. "Robust Regression." In Advanced Statistics for the Behavioral Sciences. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93549-2_7.

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Awange, Joseph L., Béla Paláncz, Robert H. Lewis, and Lajos Völgyesi. "Robust Regression." In Mathematical Geosciences. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-92495-9_14.

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Rao, Calyampudi Radhakrishna, and Helge Toutenburg. "Robust Regression." In Springer Series in Statistics. Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4899-0024-1_9.

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Mammen, Enno. "Bootstrapping robust regression." In When Does Bootstrap Work? Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2950-6_8.

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Conference papers on the topic "Robust regression"

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Shoji, Yoshifumi, and Masahiro Yukawa. "Robust Quantile Regression Under Unreliable Data." In 2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2024. https://doi.org/10.1109/apsipaasc63619.2025.10849248.

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Bitar, Ahmad W. "Robust European Call Option Pricing via Linear Regression." In 2025 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics Companion (CiFer Companion). IEEE, 2025. https://doi.org/10.1109/cifercompanion65204.2025.10980400.

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Diskin, Tzvi, Gordana Draskovic, Frederic Pascal, and Ami Wiesel. "Deep robust regression." In 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2017. http://dx.doi.org/10.1109/camsap.2017.8313200.

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Holcomb, Tyler R., and Manfred Morari. "Significance Regression: Robust Regression for Collinear Data." In 1993 American Control Conference. IEEE, 1993. http://dx.doi.org/10.23919/acc.1993.4793203.

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Simakhin, V. A. "Nonparametric robust regression estimate." In SPIE Proceedings, edited by Gennadii G. Matvienko and Victor A. Banakh. SPIE, 2006. http://dx.doi.org/10.1117/12.723206.

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Naseem, Imran, Roberto Togneri, and Mohammed Bennamoun. "Robust Regression for Face Recognition." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.289.

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Yang, Yangzhuoran Fin, and Ziping Zhao. "Online Robust Reduced-Rank Regression." In 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2020. http://dx.doi.org/10.1109/sam48682.2020.9104268.

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Yin Wang, Caglayan Dicle, Mario Sznaier, and Octavia Camps. "Self Scaled Regularized Robust Regression." In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015. http://dx.doi.org/10.1109/cvpr.2015.7298946.

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Wang, Ziyu, Jiahai Yang, Zhang ShiZe, and Chenxi Li. "Robust regression for anomaly detection." In ICC 2017 - 2017 IEEE International Conference on Communications. IEEE, 2017. http://dx.doi.org/10.1109/icc.2017.7997373.

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Belagiannis, Vasileios, Christian Rupprecht, Gustavo Carneiro, and Nassir Navab. "Robust Optimization for Deep Regression." In 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015. http://dx.doi.org/10.1109/iccv.2015.324.

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Reports on the topic "Robust regression"

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Morgenthaler, S., and R. Maronna. Robust Regression through Robust Covariances. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada153301.

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Finch, Holmes. Robust Methods for Regression Modeling. Instats Inc., 2025. https://doi.org/10.61700/id1rmtgty93xq1812.

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This two-day seminar provides a comprehensive exploration of robust regression methods, focusing on their application when traditional regression assumptions are violated. Participants will learn to implement nonparametric and robust techniques using R, enhancing the reliability and validity of their research findings.
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Shen, Andy, Devin Francom, and Kelin Rumsey. Robust Bayesian Multivariate Adaptive Regression Splines (BMARS). Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1671070.

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Baillie, Richard, Francis Diebold, George Kapetanios, Kun Ho Kim, and Aaron Mora. On Robust Inference in Time Series Regression. National Bureau of Economic Research, 2024. http://dx.doi.org/10.3386/w32554.

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Stock, James, and Mark Watson. Heteroskedasticity-Robust Standard Errors for Fixed Effects Panel Data Regression. National Bureau of Economic Research, 2006. http://dx.doi.org/10.3386/t0323.

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Belloni, Alexandre, Victor Chernozhukov, and Kengo Kato. Robust inference in high-dimensional approximately sparse quantile regression models. IFS, 2013. http://dx.doi.org/10.1920/wp.cem.2013.7013.

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Carroll, R. J., and David Ruppert. Diagnostics and Robust Estimation When Transforming the Regression Model and the Response. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada177531.

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Kunsch, H. R., L. A. Stefanski, and R. J. Carroll. Conditionally Unbiased Bounded Influence Robust Regression with Applications to Generalized Linear Models. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada186319.

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Carroll, R. J., and David Ruppert. Diagnostics and Robust Estimation When Transforming the Regression Model and the Responses. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada171938.

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Simpson, James R. A Combined Biased-Robust Estimator for Dealing with Influence and Collinearity in Regression. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada281793.

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