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Relevant bibliographies by topics / Piecewise linear regression

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Academic literature on the topic 'Piecewise linear regression'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Piecewise linear regression"

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Nakamura, Tsuyoshi. "BMDP program for piecewise linear regression." Computer Methods and Programs in Biomedicine 23, no. 1 (1986): 53–55. http://dx.doi.org/10.1016/0169-2607(86)90080-5.

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Noskov, Sergey I. "Estimating the parameters of simple nested piecele-linear regression with a linear component." Yugra State University Bulletin 20, no. 1 (2024): 19–21. http://dx.doi.org/10.18822/byusu20240119-21.

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Subject of research: the problem of estimating the parameters of a simple nested piecewise linear regression with a linear component. Purpose of research: to apply an effective linear-Boolean programming apparatus to solve this problem. Methods and objects of research: the object of research is the minimization of approximation errors of simple nested piecewise linear regression with a linear component, methods – linear regression analysis and mathematical programming apparatus. Main results of research: an approach to determining parameter estimates for simple nested piecewise linear regressi

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Носков, Сергей Иванович, and Б. Даниил Е. "Construction of a piecewise linear regression model with linear combinations of grouped predictors." Scientific works of KubSTU, no. 1 (June 6, 2024): 57–64. http://dx.doi.org/10.26297/2312-9409.2024.1.7.

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В работе приведен краткий обзор результатов по применению более сложных по сравнению с линейными форм связи между переменными в математических моделях регрессионного типа. Это, в частности, робастная модель кусочно-линейной регрессии с неизвестным количеством точек переключения; полуконтролируемый классификатор, основанный на модели кусочно-линейной регрессии; метод автоматического построения управляемых моделей энергетических систем; алгоритм решения задач многомерной регрессии и классификации с использованием кусочно-линейных предикторов над многогранным разбиением пространства пр

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Yang, Xubing, Hongxin Yang, Fuquan Zhang, et al. "Piecewise Linear Regression Based on Plane Clustering." IEEE Access 7 (2019): 29845–55. http://dx.doi.org/10.1109/access.2019.2902620.

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Krotov, Sergey V., Dmitriy P. Kononov, and Alexander P. Buynosov. "Piecewise linear regression for estimating slip zones." Transport of the Urals, no. 1 (2024): 30–34. http://dx.doi.org/10.20291/1815-9400-2024-1-30-34.

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A strong connection between a wheel and an axle is an important parameter for the load-bearing capacity of a wheelset. While operating mutual slip zones of the wheel and axle reduce the reliability of the connection and their direct measurement is not possible. The article examines the admissibility of using piecewise linear regression with a breakpoint to predict the state of a press connection in which slip zones can reach significant values. This is a danger for traffic safety especially while running rolling stock with increased axle loads. The wheelset parameter is calculated using the fi

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Yang, Lingjian, Songsong Liu, Sophia Tsoka, and Lazaros G. Papageorgiou. "Mathematical programming for piecewise linear regression analysis." Expert Systems with Applications 44 (February 2016): 156–67. http://dx.doi.org/10.1016/j.eswa.2015.08.034.

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Gopalswamy, Karthick, Yahya Fathi, and Reha Uzsoy. "Valid inequalities for concave piecewise linear regression." Operations Research Letters 47, no. 1 (2019): 52–58. http://dx.doi.org/10.1016/j.orl.2018.12.004.

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Noskov, S. I., and A. S. Vergasov. "PIECEWISE LINEAR REGRESSION MODEL OF ROAD CONSTRUCTION." Advances and Applications in Discrete Mathematics 39, no. 1 (2023): 117–24. http://dx.doi.org/10.17654/0974165823040.

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Zhang, Tianyi. "Statistical Estimation in Piecewise Linear Regression Models." Asian Research Journal of Mathematics 21, no. 4 (2025): 39–45. https://doi.org/10.9734/arjom/2025/v21i4909.

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The kink regression model assumes that linear regression forms are separately modelled on two sides of an unknown threshold but still continuous at the threshold. This paper considers statistical estimation for piecewise linear regression models which are widely used in various fields to capture nonlinear relationships between variables. The estimators for the kink locations and regression coefficients are obtained by using the least squares method, a detailed explanation of the estimation process is provided. Furthermore, the proposed methodology is validated through an illustrative example u

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Debarre, Thomas, Quentin Denoyelle, Michael Unser, and Julien Fageot. "Sparsest piecewise-linear regression of one-dimensional data." Journal of Computational and Applied Mathematics 406 (May 2022): 114044. http://dx.doi.org/10.1016/j.cam.2021.114044.

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Dissertations / Theses on the topic "Piecewise linear regression"

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Gormley, Nolan D. "Knotilus: A Differentiable Piecewise Linear Regression Framework." Bowling Green State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1617222994436272.

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FASOLA, Salvatore. "Change-point estimation in piecewise constant regression models and extensions." Doctoral thesis, Università degli Studi di Palermo, 2015. http://hdl.handle.net/10447/105107.

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Pettersson, Angelica. "Något om regressionsanalys." Thesis, Örebro University, School of Science and Technology, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-10705.

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<p>En gren inom statistikteorin är den så kallade Regressionsanalysen där man studerar hur data från exempelvis ett stickprov kan anpassas till en graf. Skrivandet av denna uppsats har haft som syfte att studera några av de metoder som finns att tillgå vid bestämning av de ingående parametrarna i de enklare fallen av regression. Dessutom ges i de avslutande kapitlen exempel på den del inom regressionsanalysen som kallas Styckvis Linjär Regression eller <em>Piecewise Linear Regression.</em></p><br>Presentationen är redan avklarad den 26 april 2010 kl. 11.30

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Van, der Westhuizen Magdelena Marianna. "Robust techniques for regression models with minimal assumptions / M.M. van der Westhuizen." Thesis, North-West University, 2011. http://hdl.handle.net/10394/6689.

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Good quality management decisions often rely on the evaluation and interpretation of data. One of the most popular ways to investigate possible relationships in a given data set is to follow a process of fitting models to the data. Regression models are often employed to assist with decision making. In addition to decision making, regression models can also be used for the optimization and prediction of data. The success of a regression model, however, relies heavily on assumptions made by the model builder. In addition, the model may also be influenced by the presence of outliers; a more robu

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Ohlsson, Henrik, and Lennart Ljung. "Identification of switched linear regression models using sum-of-norms regularization." Linköpings universitet, Reglerteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-92612.

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This paper proposes a general convex framework for the identification of switched linear systems. The proposed framework uses over-parameterization to avoid solving the otherwise combinatorially forbidding identification problem, and takes the form of a least-squares problem with a sum-of-norms regularization, a generalization of the ℓ1-regularization. The regularization constant regulates the complexity and is used to trade off the fit and the number of submodels.<br><p>Funding Agencies|Swedish foundation for strategic research in the center MOVIII||Swedish Research Council in the Linnaeus ce

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Sousa, F. Raquel R. F. de. "Exploratory spatial analysis of topographic surface metrics for the prediction of water table occurrence." Master's thesis, Universidade de Évora, 2014. http://hdl.handle.net/10174/12215.

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Starting from the premise that water table in aquifers follows topographic surface as well as underground flow direction tends to be consistent with the surface streams flow directions, the present work presents the essays of the research to define a model that predicts, through a piecewise multiple regression, groundwater level in the Estremoz-Cano Aquifer System and in the surrounding igneous and metamorphic rocks of the OMZ as a function of topography, namely a set of terrain metrics like curvature and structural curvature to be related with the static water level (SWL) measured on dug well

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Huang, Min Ching. "Piecewise linear tree-structured regression." 1989. http://catalog.hathitrust.org/api/volumes/oclc/21951798.html.

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Thesis (Ph. D.)--University of Wisconsin--Madison, 1989.<br>Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 101-104).

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Kademan, Edmund John. "Piecewise linear regression through random partitioning." 1993. http://catalog.hathitrust.org/api/volumes/oclc/31038585.html.

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Thesis (Ph. D.)--University of Wisconsin--Madison, 1993.<br>Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 68-69).

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Manwani, Naresh. "Supervised Learning of Piecewise Linear Models." Thesis, 2012. http://etd.iisc.ac.in/handle/2005/3244.

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Supervised learning of piecewise linear models is a well studied problem in machine learning community. The key idea in piecewise linear modeling is to properly partition the input space and learn a linear model for every partition. Decision trees and regression trees are classic examples of piecewise linear models for classification and regression problems. The existing approaches for learning decision/regression trees can be broadly classified in to two classes, namely, fixed structure approaches and greedy approaches. In the fixed structure approaches, tree structure is fixed before hand by

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Manwani, Naresh. "Supervised Learning of Piecewise Linear Models." Thesis, 2012. http://hdl.handle.net/2005/3244.

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Supervised learning of piecewise linear models is a well studied problem in machine learning community. The key idea in piecewise linear modeling is to properly partition the input space and learn a linear model for every partition. Decision trees and regression trees are classic examples of piecewise linear models for classification and regression problems. The existing approaches for learning decision/regression trees can be broadly classified in to two classes, namely, fixed structure approaches and greedy approaches. In the fixed structure approaches, tree structure is fixed before hand by

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Books on the topic "Piecewise linear regression"

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Siu, Cynthia Oi Ling. Piecewise linear tree-structured regression with an application to the removal of confounding effects. 1985.

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Siu, Cynthia Oi Ling. Piecewise linear tree-structured regression with an application to the removal of confounding effects. 1985.

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Book chapters on the topic "Piecewise linear regression"

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Warwicker, John Alasdair, and Steffen Rebennack. "Univariate Continuous Piecewise Linear Regression." In Encyclopedia of Optimization. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-54621-2_727-1.

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Ferrari-Trecate, Giancarlo, Marco Muselli, Diego Liberati, and Manfred Morari. "A Learning Algorithm for Piecewise Linear Regression." In Perspectives in Neural Computing. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0219-9_9.

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Ferrari-Trecate, Giancarlo, and Marco Muselli. "A New Learning Method for Piecewise Linear Regression." In Artificial Neural Networks — ICANN 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_72.

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Vannucci, Giulia, Anna Gottard, Leonardo Grilli, and Carla Rampichini. "Random effects regression trees for the analysis of INVALSI data." In Proceedings e report. Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-304-8.07.

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Mixed or multilevel models exploit random effects to deal with hierarchical data, where statistical units are clustered in groups and cannot be assumed as independent. Sometimes, the assumption of linear dependence of a response on a set of explanatory variables is not plausible, and model specification becomes a challenging task. Regression trees can be helpful to capture non-linear effects of the predictors. This method was extended to clustered data by modelling the fixed effects with a decision tree while accounting for the random effects with a linear mixed model in a separate step (Hajje

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Li, Jia-Heng, Xiao-Lin Zhou, Rong-Chao Peng, and Feng Lv. "An Adaptive Compression Algorithm for Wireless Sensor Network Based on Piecewise Linear Regression." In International Conference on Biomedical and Health Informatics. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-4505-9_7.

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Makowski, Greg. "A more flexible method for recognizing signals using back propagation: Piecewise linear regression vectors." In Computing in the 90's. Springer New York, 1991. http://dx.doi.org/10.1007/bfb0038480.

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Jiang, Pengchun, and Mu Zhou. "LTE Antenna Port Number Detection Algorithm Based on Channel Estimation and Piecewise Linear Regression." In Machine Learning and Intelligent Communications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32388-2_4.

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Li, Juntao, Kwok Pui Choi, and R. Krishna Murthy Karuturi. "Iterative Piecewise Linear Regression to Accurately Assess Statistical Significance in Batch Confounded Differential Expression Analysis." In Bioinformatics Research and Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30191-9_15.

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Schouten, Rianne M., Wouter Duivesteijn, Pekka Räsänen, Jacob M. Paul, and Mykola Pechenizkiy. "Exceptional Subitizing Patterns: Exploring Mathematical Abilities of Finnish Primary School Children with Piecewise Linear Regression." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70381-2_5.

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"APPENDIX A. The Piecewise Linear Regression Method." In Baseball's All-Time Best Sluggers. Princeton University Press, 2005. http://dx.doi.org/10.1515/9781400881352-020.

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Conference papers on the topic "Piecewise linear regression"

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Bisserier, A., S. Galichet, and R. Boukezzoula. "Fuzzy piecewise linear regression." In 2008 IEEE 16th International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2008. http://dx.doi.org/10.1109/fuzzy.2008.4630658.

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Perez-Pellitero, Eduardo, Jordi Salvador, Javier Ruiz-Hidalgo, and Bodo Rosenhahn. "Half hypersphere confinement for piecewise linear regression." In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2016. http://dx.doi.org/10.1109/wacv.2016.7477651.

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Maragos, Petros, and Emmanouil Theodosis. "Multivariate Tropical Regression and Piecewise-Linear Surface Fitting." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054058.

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Zeitler, Georg C., Andrew C. Singer, and Suleyman S. Kozat. "Universal Piecewise Linear Regression of Individual Sequences: Lower Bound." In 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.366811.

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Li, Zhihui, Shuai Wang, Yongmei Liu, Yufei Huang, and Jing Zhang. "Fast Multi-output Regression Based on Piecewise Linear Approximation." In 2020 7th International Conference on Information Science and Control Engineering (ICISCE). IEEE, 2020. http://dx.doi.org/10.1109/icisce50968.2020.00371.

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Shi, Yu, Jian Li, and Zhize Li. "Gradient Boosting with Piece-Wise Linear Regression Trees." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/476.

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Gradient Boosted Decision Trees (GBDT) is a very successful ensemble learning algorithm widely used across a variety of applications. Recently, several variants of GBDT training algorithms and implementations have been designed and heavily optimized in some very popular open sourced toolkits including XGBoost, LightGBM and CatBoost. In this paper, we show that both the accuracy and efficiency of GBDT can be further enhanced by using more complex base learners. Specifically, we extend gradient boosting to use piecewise linear regression trees (PL Trees), instead of piecewise constant regression

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Kuroda, Hiroki, and Jun Ogata. "Piecewise Linear Regression under Noise Level Variation via Convex Optimization." In 2020 28th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco47968.2020.9287844.

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Vanli, N. Denizcan, Muhammed O. Sayin, Tolga Gozey, and Suleyman S. Kozat. "Twice-universal piecewise linear regression via infinite depth context trees." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178331.

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Onder, Zeynep, Ali Degirmenci, and Omer Karal. "Estimating Breakpoints in Piecewise Linear Regression Using Machine Learning Methods." In 2022 Innovations in Intelligent Systems and Applications Conference (ASYU). IEEE, 2022. http://dx.doi.org/10.1109/asyu56188.2022.9925406.

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Zhao, Luyang, Josiah Putman, Weifu Wang, and Devin Balkcom. "PLRC*: A piecewise linear regression complex for approximating optimal robot motion." In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020. http://dx.doi.org/10.1109/iros45743.2020.9341312.

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Reports on the topic "Piecewise linear regression"

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Quan, Lin. Piecewise linear regression for leaf appearance rate data. Iowa State University, 2021. http://dx.doi.org/10.31274/cc-20240624-1124.

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Cattaneo, Matias D., Richard K. Crump, Max H. Farrell, and Yingjie Feng. Nonlinear Binscatter Methods. Federal Reserve Bank of New York, 2024. http://dx.doi.org/10.59576/sr.1110.

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Binned scatter plots are a powerful statistical tool for empirical work in the social, behavioral, and biomedical sciences. Available methods rely on a quantile-based partitioning estimator of the conditional mean regression function to primarily construct flexible yet interpretable visualization methods, but they can also be used to estimate treatment effects, assess uncertainty, and test substantive domain-specific hypotheses. This paper introduces novel binscatter methods based on nonlinear, possibly nonsmooth M-estimation methods, covering generalized linear, robust, and quantile regressio

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R.P. Ewing and D.W. Meek. One Line or Two? Perspectives on Piecewise Regression. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/899336.

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