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Journal articles on the topic 'Clusterwise linear regression'

Author: Grafiati

Published: 19 February 2023

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1

Li, Ting, Xinyuan Song, Yingying Zhang, Hongtu Zhu, and Zhongyi Zhu. "Clusterwise functional linear regression models." Computational Statistics & Data Analysis 158 (June 2021): 107192. http://dx.doi.org/10.1016/j.csda.2021.107192.

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2

Galimberti, Giuliano, and Gabriele Soffritti. "Seemingly unrelated clusterwise linear regression." Advances in Data Analysis and Classification 14, no. 2 (August 12, 2019): 235–60. http://dx.doi.org/10.1007/s11634-019-00369-4.

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3

Joki, Kaisa, Adil M. Bagirov, Napsu Karmitsa, Marko M. Mäkelä, and Sona Taheri. "Clusterwise support vector linear regression." European Journal of Operational Research 287, no. 1 (November 2020): 19–35. http://dx.doi.org/10.1016/j.ejor.2020.04.032.

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4

Park, Young Woong, Yan Jiang, Diego Klabjan, and Loren Williams. "Algorithms for Generalized Clusterwise Linear Regression." INFORMS Journal on Computing 29, no. 2 (May 2017): 301–17. http://dx.doi.org/10.1287/ijoc.2016.0729.

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5

Späth, H. "Clusterwise linear least absolute deviations regression." Computing 37, no. 4 (December 1986): 371–77. http://dx.doi.org/10.1007/bf02251095.

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6

García-Escudero, L. A., A. Gordaliza, A. Mayo-Iscar, and R. San Martín. "Robust clusterwise linear regression through trimming." Computational Statistics & Data Analysis 54, no. 12 (December 2010): 3057–69. http://dx.doi.org/10.1016/j.csda.2009.07.002.

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7

Wedel, Michel, and Cor Kistemaker. "Consumer benefit segmentation using clusterwise linear regression." International Journal of Research in Marketing 6, no. 1 (September 1989): 45–59. http://dx.doi.org/10.1016/0167-8116(89)90046-3.

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8

Hennig, C. "Identifiablity of Models for Clusterwise Linear Regression." Journal of Classification 17, no. 2 (July 1, 2000): 273–96. http://dx.doi.org/10.1007/s003570000022.

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9

Khadka, Mukesh, and Alexander Paz. "Comprehensive Clusterwise Linear Regression for Pavement Management Systems." Journal of Transportation Engineering, Part B: Pavements 143, no. 4 (December 2017): 04017014. http://dx.doi.org/10.1061/jpeodx.0000009.

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10

Di Mari, Roberto, Roberto Rocci, and Stefano Antonio Gattone. "Clusterwise linear regression modeling with soft scale constraints." International Journal of Approximate Reasoning 91 (December 2017): 160–78. http://dx.doi.org/10.1016/j.ijar.2017.09.006.

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11

Cohen, Pablo L. Durango, Elizabeth J. Durango Cohen, and Weizeng Zhang. "A clusterwise linear regression model of alumni giving." International Journal of Education Economics and Development 3, no. 4 (2012): 330. http://dx.doi.org/10.1504/ijeed.2012.052323.

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12

DeSarbo, Wayne S., Richard L. Oliver, and Arvind Rangaswamy. "A simulated annealing methodology for clusterwise linear regression." Psychometrika 54, no. 4 (September 1989): 707–36. http://dx.doi.org/10.1007/bf02296405.

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13

DeSarbo, Wayne S., and William L. Cron. "A maximum likelihood methodology for clusterwise linear regression." Journal of Classification 5, no. 2 (September 1988): 249–82. http://dx.doi.org/10.1007/bf01897167.

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14

Zhang, Weizeng, and Pablo L. Durango-Cohen. "Explaining Heterogeneity in Pavement Deterioration: Clusterwise Linear Regression Model." Journal of Infrastructure Systems 20, no. 2 (June 2014): 04014005. http://dx.doi.org/10.1061/(asce)is.1943-555x.0000182.

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15

Meier, J. "A fast algorithm for clusterwise linear absolute deviations regression." OR Spektrum 9, no. 3 (September 1987): 187–89. http://dx.doi.org/10.1007/bf01721102.

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16

Bagirov, Adil M., Julien Ugon, and Hijran Mirzayeva. "Nonsmooth nonconvex optimization approach to clusterwise linear regression problems." European Journal of Operational Research 229, no. 1 (August 2013): 132–42. http://dx.doi.org/10.1016/j.ejor.2013.02.059.

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17

Bagirov, Adil M., Julien Ugon, and Hijran G. Mirzayeva. "Nonsmooth Optimization Algorithm for Solving Clusterwise Linear Regression Problems." Journal of Optimization Theory and Applications 164, no. 3 (April 15, 2014): 755–80. http://dx.doi.org/10.1007/s10957-014-0566-y.

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18

Butar-butar, Victor Pandapotan, Agus M. Soleh, and Aji H. Wigena. "PEMODELAN CLUSTERWISE REGRESSION PADA STATISTICAL DOWNSCALING UNTUK PENDUGAAN CURAH HUJAN BULANAN." Indonesian Journal of Statistics and Its Applications 3, no. 3 (October 31, 2019): 236–46. http://dx.doi.org/10.29244/ijsa.v3i3.310.

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Statistical downscaling (SDS) is one of the developing models for rainfall estimation. The SDS model is a regression model used to analyze the relation of global (GCM output) and local data (rainfall). Rainfall has large variance so that clustering is needed to minimize the variance. One of the analytical methods that can be used in clustering rainfall estimation is cluster wise regression. There are three Methods for Clusterwise regression namely Linear Regresion, Finite Mixture Method (FMM) and Cluster-Weighted Method (CWM). This study used GCM outputs data namely CFRSv2 as a covariate. The response variable is rainfall data in four stations such as Bandung, Bogor, Citeko and Jatiwangi from BMKG. The purpose of this study is to increase the accuracy of rainfall estimation using the three methods and compare the clusterwise regression with PCR and PLS models. Based on the value of RMSEP, the clusterwise regression with FMM was the best method to estimate rainfall in four stations.

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19

da Silva, Ricardo A. M., and Francisco de A. T. de Carvalho. "Weighted Clusterwise Linear Regression based on adaptive quadratic form distance." Expert Systems with Applications 185 (December 2021): 115609. http://dx.doi.org/10.1016/j.eswa.2021.115609.

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20

D’Urso, Pierpaolo, and Adriana Santoro. "Fuzzy clusterwise linear regression analysis with symmetrical fuzzy output variable." Computational Statistics & Data Analysis 51, no. 1 (November 2006): 287–313. http://dx.doi.org/10.1016/j.csda.2006.06.001.

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21

Bagirov, Adil M., Julien Ugon, and Hijran G. Mirzayeva. "An algorithm for clusterwise linear regression based on smoothing techniques." Optimization Letters 9, no. 2 (May 14, 2014): 375–90. http://dx.doi.org/10.1007/s11590-014-0749-3.

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22

Ismail, Eman, Mahmoud Rashwan, and Nadia Makary. "A generalized goal programming model for parsimonious robust clusterwise linear regression." Journal of Statistics and Management Systems 22, no. 1 (January 2, 2019): 51–71. http://dx.doi.org/10.1080/09720510.2018.1522801.

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23

He, L., G. H. Huang, and H. W. Lu. "Health-Risk-Based Groundwater Remediation System Optimization through Clusterwise Linear Regression." Environmental Science & Technology 42, no. 24 (December 15, 2008): 9237–43. http://dx.doi.org/10.1021/es800834x.

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24

Bagirov, Adil M., Arshad Mahmood, and Andrew Barton. "Prediction of monthly rainfall in Victoria, Australia: Clusterwise linear regression approach." Atmospheric Research 188 (May 2017): 20–29. http://dx.doi.org/10.1016/j.atmosres.2017.01.003.

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25

Bagirov, Adil M., and Sona Taheri. "DC Programming Algorithm for Clusterwise Linear $${{\varvec{L}}}_\mathbf{1}$$ Regression." Journal of the Operations Research Society of China 5, no. 2 (March 17, 2017): 233–56. http://dx.doi.org/10.1007/s40305-017-0151-9.

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26

Bagirov, A. M., and J. Ugon. "Nonsmooth DC programming approach to clusterwise linear regression: optimality conditions and algorithms." Optimization Methods and Software 33, no. 1 (September 14, 2017): 194–219. http://dx.doi.org/10.1080/10556788.2017.1371717.

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27

Brusco, Michael J., J. Dennis Cradit, and Armen Tashchian. "Multicriterion Clusterwise Regression for Joint Segmentation Settings: An Application to Customer Value." Journal of Marketing Research 40, no. 2 (May 2003): 225–34. http://dx.doi.org/10.1509/jmkr.40.2.225.19227.

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The authors present a multicriterion clusterwise linear regression model that can be applied to a joint segmentation setting. The model enables the consideration of segment homogeneity, as well as multiple dependent variables (segmentation bases), in a weighted objective function. The authors propose a heuristic solution strategy based on simulated annealing and examine trade-offs in the recovery of multiple true cluster structures for several synthetic data sets. They also propose an application of the model to a joint segmentation problem in the telecommunications industry, which addresses important issues pertaining to the selection of the objective function weights and the number of clusters.

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28

Di Mari, Roberto, Roberto Rocci, and Stefano Antonio Gattone. "Scale-constrained approaches for maximum likelihood estimation and model selection of clusterwise linear regression models." Statistical Methods & Applications 29, no. 1 (June 25, 2019): 49–78. http://dx.doi.org/10.1007/s10260-019-00480-y.

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29

Wang, S., G. H. Huang, and L. He. "Development of a clusterwise-linear-regression-based forecasting system for characterizing DNAPL dissolution behaviors in porous media." Science of The Total Environment 433 (September 2012): 141–50. http://dx.doi.org/10.1016/j.scitotenv.2012.06.045.

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30

Khadka, Mukesh, Alexander Paz, Cristian Arteaga, and David K. Hale. "Simultaneous Generation of Optimum Pavement Clusters and Associated Performance Models." Mathematical Problems in Engineering 2018 (December 12, 2018): 1–17. http://dx.doi.org/10.1155/2018/2159865.

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With regard to developing pavement performance models (PPMs), the existing state-of-the-art proposes Clusterwise Linear Regression (CLR) to determine the pavement clusters and associated PPMs simultaneously. However, the approach does not determine optimal clustering to minimize error; that is, the number of clusters and explanatory variables are prespecified to determine the corresponding coefficients of the PPMs. In addition, existing formulations do no address issues associated with overfitting as there is no limit to include parameters in the model. In order to address this limitation, this paper proposes a mathematical program within the CLR approach to determine simultaneously (1) an optimal number of clusters, (2) assignment of segments into clusters, and (3) regression coefficients for all prespecified explanatory variables required to minimize the estimation error. The Bayesian Information Criteria is proposed to limit the number of optimal clusters. A simulated annealing coupled with ordinary least squares was used to solve the mathematical program.

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31

Anacona-Campo, Francisco Javier, Carlos-Alberto Cobos-Lozada, and Martha Mendoza-Becerra. "Algoritmo greedy para predecir el índice de servicio de pavimento basado en agrupación y regresión lineal." Investigación e Innovación en Ingenierías 8, no. 3 (November 23, 2020): 119–34. http://dx.doi.org/10.17081/invinno.8.3.4708.

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Objetivo: Proponer un algoritmo CLR (Clusterwise Linear Regression) que realiza agrupamiento divisivo de muestras de segmentos de pavimentos utilizando modelos de regresión lineal y define automáticamente el número de agrupaciones con el fin de predecir el índice de capacidad de servicio del pavimento (pavement serviceability index, PSI). Metodología: Basado en el proceso de investigación iterativa propuesto por Pratt se desarrollaron dos ciclos de mejora del algoritmo propuesto. El primer ciclo permitió obtener una versión inicial, aplicarlo sobre los datasets de entrenamiento y prueba y observar las mejoras que se debían realizar. En el segundo ciclo se obtuvo la versión final a la que se le afinaron los parámetros y se comparó con el estado del arte usando varias métricas. Resultados: Se obtuvo un modelo compuesto por tres grupos de muestras de segmentos de pavimento con sus correspondientes modelos de regresión lineal multivariable (atributos mixtos) que permiten predecir el PSI de una muestra de pavimento. Conclusiones: El modelo se obtuvo con menor tiempo de cómputo (15,6 veces menos tiempo que el reportado por el estado del arte) y presenta mejores resultados en sencillez en comparación con los modelos lineales y no lineales reportados en la literatura, además, en calidad tiene resultados similares (incluso mejores en algunas métricas) al modelo lineal y es competitivo frente al modelo no lineal.

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32

Long, Qiang, Adil M. Bagirov, Sona Taheri, Nargiz Sultanova, and Xue Wu. "Methods and Applications of Clusterwise Linear Regression." ACM Transactions on Knowledge Discovery from Data, July 26, 2022. http://dx.doi.org/10.1145/3550074.

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Clusterwise linear regression (CLR) is a well-known technique for approximating a data using more than one linear function. It is based on the combination of clustering and multiple linear regression methods. This paper provides a comprehensive survey and comparative assessments of CLR including model formulations, description of algorithms and their performance on small to large-scale synthetic and real-world data sets. Some applications of the CLR algorithms and possible future research directions are also discussed.

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33

Karmitsa, Napsu, Sona Taheri, Adil Bagirov, and Pauliina Makinen. "Missing Value Imputation via Clusterwise Linear Regression." IEEE Transactions on Knowledge and Data Engineering, 2020, 1. http://dx.doi.org/10.1109/tkde.2020.3001694.

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34

Perrone, Gabriele, and Gabriele Soffritti. "Seemingly unrelated clusterwise linear regression for contaminated data." Statistical Papers, August 6, 2022. http://dx.doi.org/10.1007/s00362-022-01344-6.

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AbstractClusterwise regression is an approach to regression analysis based on finite mixtures which is generally employed when sample observations come from a population composed of several unknown sub-populations. Whenever the response is continuous, Gaussian clusterwise linear regression models are usually employed. Such models have been recently robustified with respect to the possible presence of mild outliers in the sub-populations. However, in some fields of research, especially in the modelling of multivariate economic data or data from the social sciences, there may be prior information on the specific covariates to be considered in the linear term employed in the prediction of a certain response. As a consequence, covariates may not be the same for all responses. Thus, a novel class of multivariate Gaussian linear clusterwise regression models is proposed. This class provides an extension to mixture-based regression analysis for modelling multivariate and correlated responses in the presence of mild outliers that let the researcher free to use a different vector of covariates for each response. Details about the model identification and maximum likelihood estimation via an expectation-conditional maximisation algorithm are given. The performance of the new models is studied by simulation in comparison with other clusterwise linear regression models. A comparative evaluation of their effectiveness and usefulness is provided through the analysis of a real dataset.

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35

Bagirov, Adil M., Sona Taheri, and Emre Cimen. "Incremental DC optimization algorithm for large-scale clusterwise linear regression." Journal of Computational and Applied Mathematics, December 2020, 113323. http://dx.doi.org/10.1016/j.cam.2020.113323.

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36

Di Mari, Roberto, Roberto Rocci, and Stefano Antonio Gattone. "LASSO-Penalized Clusterwise Linear Regression Modeling With Local Least Angle Regression (L-LARS)." SSRN Electronic Journal, 2021. http://dx.doi.org/10.2139/ssrn.3832769.

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37

Galimberti, Giuliano, Lorenzo Nuzzi, and Gabriele Soffritti. "Covariance matrix estimation of the maximum likelihood estimator in multivariate clusterwise linear regression." Statistical Methods & Applications, May 18, 2020. http://dx.doi.org/10.1007/s10260-020-00523-9.

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38

N. C. A. Lima, Marília, and Roberta A. de A. Fagundes. "Educational DataMining: A Study of the Factors That Cause School Dropout in Higher Education Institutions in Brazil." RENOTE 18, no. 1 (July 31, 2020). http://dx.doi.org/10.22456/1679-1916.105950.

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Context:In Brazil, there is a high dropout rate in higher education institutions. Thus, it is clear that evasion is a frequent problem and that it is necessary to analyze the factors that cause it to enable solutions that can mitigate/ reduce this problem. Objetive: (1)perform a correlation analysis (Pearson and Spearman) of the educational factores of the School Census; (2)propose school dropout prediction models taking into account educational and economic factors using regression methods (linear, robust, ridge, lasso, clusterwise regression). Methodology: used the phases of the CRISP-DM methodology. Results: the factors related to not allowing financial assistance are related to as evasion, namely: food, permanence, didactic material, transportation. There are also factors related to the study period. The regression robust and linear regression show fewer errors. Conclusion: the correlations used present the selection of factors in a similar way, thus following a linear distribution. This study can help to create more investment in public policies, as it ratifies factors are related to this dropout problem.

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