Multivariate response directional regression: a projective resampling approach by Ahreum Lee, Kyongwon Kim

“…Abstract In high dimensional data analysis, directional regression is a widely used method for implementing linear sufficient dimension reduction by extracting core information from the complex data structure. …”
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Variable Selection for Additive Quantile Regression with Nonlinear Interaction Structures by Yongxin Bai, Jiancheng Jiang, Maozai Tian

“…In high-dimensional data analysis, main effects and interaction effects often coexist, especially when complex nonlinear relationships are present. Effective variable selection is crucial for avoiding the curse of dimensionality and enhancing the predictive performance of a model. …”
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Optimizing compressive strength of foamed concrete using stepwise regression by Iman Kattoof Harith, Ehsan Elewy Salman, Mohammed L. Hussien, Ahmed Y. Mohammed, Wissam Nadir

“…Abstract Foamed concrete (FC) is distinguished by its unique properties and complex mixture design, which often necessitates extensive experimental trials to achieve target characteristics such as compressive strength (CS). …”
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The New Generalized Exponentiated Fréchet–Weibull Distribution: Properties, Applications, and Regression Model by Hadeel S. Klakattawi, Aisha A. Khormi, Lamya A. Baharith

“…A new distribution is therefore required in order to accommodate the complexities of different data shapes and enhance the goodness of fit. …”
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Model and Variable Selection Procedures for Semiparametric Time Series Regression by Risa Kato, Takayuki Shiohama

“…Semiparametric regression models are very useful for time series analysis. …”
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ICBoost: An XGBoost-Based Unbiased Transformed Algorithm for Survival Regression by Jingyi Zhang, Shishun Zhao, Yang Xu, Tao Hu

“…Therefore, we present ICBoost, a novel survival algorithm that integrates regression trees and ensemble methods specifically designed for interval-censored data. …”
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Machine learning models for estimating criteria pollutants and health risk-based air quality indices over eastern coast coal mine complex belts by Pradeep Kumar, Arti Choudhary, P. K. Joshi, Ram Pravesh Kumar, R. Bhatla

“…This study aims to evaluate the variation, estimate the levels of criteria pollutants, and assess their potential health risks in the vicinity of a coal mine complex and a thermal power plant situated in an eastern coastal state of India. …”
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Unsupervised feature selection based on generalized regression model with linear discriminant constraints by Xiangguang Dai, Mingyu Guan, Facheng Dai, Wei Zhang, Tingji Zhang, Hangjun Che, Xiangqin Dai

“…Abstract Unsupervised feature selection (UFS) methods play a crucial role in improving the efficiency of extracting relevant information and reducing computational complexity in the context of big data analysis. Despite notable advancements in the field of unsupervised feature selection for large-scale datasets, many UFS methods still remain redundant and irrelevant features during the feature selection process. …”
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Learning interpretable network dynamics via universal neural symbolic regression by Jiao Hu, Jiaxu Cui, Bo Yang

“…In this work, we develop a universal computational tool that can automatically, efficiently, and accurately learn the symbolic patterns of changes in complex system states by combining the excellent fitting capability of deep learning with the equation inference ability of pre-trained symbolic regression. …”
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Reliability Optimization of Structural Deformation with Improved Support Vector Regression Model by Zheng-Zheng Zhu, Yun-Wen Feng, Cheng Lu, Cheng-Wei Fei

“…The works of this paper provide a machine learning-based reliability optimization approach for the reliability optimization of complex structures and enrich and develop mechanical reliability theory and methods.…”
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Preoperative prediction of gastric cancer T-staging based on ordinal regression models by O. V. Krasko, M. Yu. Reutovich, A. L. Patseika

“…Objectives. Study of ordinal regressions presented via the set of binary logistic regressions and their application in clinical practice for T-staging of gastric cancer.Methods. …”
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OVERCOMING OVERFITTING IN MONKEY VOCALIZATION CLASSIFICATION: USING LSTM AND LOGISTIC REGRESSION by Suryasatriya Trihandaru, Hanna Arini Parhusip, Abdiel Wilyar Goni

“…These results indicate that for such a classification problem, logistic regression may be more appropriate than the complex architecture of LSTMs. …”
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Imputasi Data Temperatur Maksimum Menggunakan Metode Support Vector Regression by Isa Kholifatus Sukhna, Brina Miftahurrohmah, Catur Wulandari, Putri Amelia

“…The choice of the SVR method is based on its ability to handle data with complex structures. Modeling is conducted separately for each station using the Radial Basis Function (RBF) kernel. …”
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Curb Detection and Mapping via Robust Iterative Gaussian Process Regression by Di Wang, Si Chen, Zhenni Ma, Jiajia Shi, Fuchun Zhang

“…Finally, the proposed iGPR is validated and tested on two large-scale, complex urban datasets under real traffic scenarios. …”
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Uncertainty quantification based on symbolic regression and probabilistic programming and its application by Yuyang Zhao, Hongbo Zhao

“…The JRC value is uncertain due to the complex, random rock joint. Uncertainty is an essential characteristic of rock joints. …”
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Dealing with adverse drug reactions in the context of polypharmacy using regression models by Jakob Sommer, Roberto Viviani, Justyna Wozniak, Julia C. Stingl, Katja S. Just

“…This study applies horseshoe and lasso regression for analyzing rare events in polypharmacy contexts, focusing on severe ADRs such as falls and bleedings. …”
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A Support Vector Regression Approach for Three–Level Longitudinal Data by Mohammad Moqaddasi Amiri, Leili Tapak, Javad Faradmal

“…In this paper a mixed-effects least squares support vector regression model is presented for three-level longitudinal data. …”
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SPATIAL MODELING OF POVERTY IN BENGKULU PROVINCE WITH MIXED GEOGRAPHICALLY WEIGHTED REGRESSION by Sigit Nugroho, Dyah Setyo Rini, Tommy Jomecho, Cinta Rizky Oktarina, Stevy Cahya Pratiwi, Elisabeth Evelin Karuna

“…This research will model the poverty of each district/city in Bengkulu Province using Mixed Geographically Weighted Regression (MGWR), because this method is quite complex in modeling data that contains spatial heterogeneity and variations in geospatial data. …”
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An Efficient Sparse Twin Parametric Insensitive Support Vector Regression Model by Shuanghong Qu, Yushan Guo, Renato De Leone, Min Huang, Pu Li

“…Overall, STPISVR enhances generalization ability, promotes sparsity, and improves prediction efficiency, making it a competitive tool for regression tasks, especially in handling complex data structures.…”
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Energy consumption forecasting and thermal insulator selection with random forest regression by Mohammed Fellah, Salma Ouhaibi, Naoual Belouaggadia, Khalifa Mansouri

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