Multivariable regression models improve accuracy and sensitive grading of antibiotic resistance mutations in Mycobacterium tuberculosis by Sanjana G. Kulkarni, Sacha Laurent, Paolo Miotto, Timothy M. Walker, Leonid Chindelevitch, Carl-Michael Nathanson, Nazir Ismail, Timothy C. Rodwell, Maha R. Farhat

“…Abstract Rapid genotype-based drug susceptibility testing for the Mycobacterium tuberculosis complex (MTBC) relies on a comprehensive knowledgebase of the genetic determinants of resistance. …”
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Dietary behaviour among adults in Northern Ghana: a polytomous logistic regression model approach by Kwame Opare-Asamoah, Daniel Edem Kpewou, Jerry Xola Sosu, Vicentia Esinam Degbey, Ezekiel Kofi Vicar, Julius T Dongdem, Saeed F. Majeed, Kwadwo Fosu Antwi

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Androgen-induced ferroptosis regulates seasonal testicular regression: insights into adaptive mechanisms of seasonal breeding by Kang An, Yuchen Tan, Ke Yang, Yukun Kang, Penghui Liu, Biaobiao Hao, Jia Kang, Junhu Su

“…Males exhibit periodic testicular development and spermatogenesis, which involves complex gene regulation and various cellular response mechanisms. …”
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Simulating the Deterioration Behavior of Tunnel Elements Using Amalgamation of Regression Trees and State-of-the-Art Metaheuristics by Eslam Mohammed Abdelkader, Abobakr Al-Sakkaf, Moaaz Elkabalawy, Abdelhady Omar, Ghasan Alfalah

“…This paper presents a novel hybrid metaheuristic-based regression tree (REGT) model designed to enhance the accuracy and robustness of tunnel deterioration predictions. …”
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Impact factor selection for non-fatal occupational injuries among manufacturing workers by LASSO regression by Yingheng XIAO, Chunhua LU, Juan QIAN, Ying CHEN, Yishuo GU, Zeyun YANG, Daozheng DING, Liping LI, Xiaojun ZHU

“…Data balancing was performed using resampling, and LASSO regression was used to select factors of non-fatal occupational injuries. …”
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Exploring a minimal Convolutional Linear-Regression Model for Urban Land Surface Temperature estimation by Matteo Piccardo, Emanuele Massaro, Luca Caporaso, Alessandro Cescatti, Grégory Duveiller

“…In response, we introduce the Convolutional Linear-Regression Model (CLRM), a minimal complexity approach that focuses on two key assumptions: (i) correlations between LST at different times and spatial resolutions are considered without additional variables, and (ii) these correlations are modelled using linear relationships. …”
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Validation of a Machine Learning Model for Certification Using Symbolic Regression and a Behaviour Envelope by Moritz Neumaier, Marcel Anselment, Stephan Rudolph

“…Since the effort for the evaluation increases with the complexity, it is proposed to use symbolic regression, a method where a search procedure combines elementary functions to create a compact symbolic model. …”
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Assessing the Impact of Geopolitical Risk on Longevity Bond Pricing: Insights from Bayesian Multivariate Regression by Yethu Sithole, Eeva Maria Rapoo, Samuel Asante Gyamerah

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Local–Linear Two-Stage Estimation of Local Autoregressive Geographically and Temporally Weighted Regression Model by Dan Xiang, Zhimin Hong

“…The two-stage weighted least squares (2SLS) estimation can effectively reduce computational complexity. However, the weighted least squares estimation is essentially a Nadaraya–Watson kernel-smoothing approach for nonparametric regression models, and it suffers from a boundary effect. …”
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Combination of Drugs in the Treatment of Alcohol Use Disorder: A Meta-Analysis and Meta-Regression Study by João Vitor Guimarães Mandaji, Maria Olivia Pozzolo Pedro, Kae Leopoldo, João Pini Alemar, Julio Torales, Antonio Ventriglio, João Mauricio Castaldelli-Maia

“…Heterogeneity was assessed using the Q test. Meta-regression was conducted using Egger’s regression method. …”
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Adjustment terms and coefficients of nonlinear regression-based kurtosis-adjusted equivalent sound level method by Jinzhe LI, Anke ZENG, Jiarui XIN, Yang LI, Linjie WU, Haiying LIU, Yan YE, Meibian ZHANG

“…Although kurtosis-adjusted equivalent sound level (\begin{document}$ {{L'}}_{\text{EX,}\text{8}\text{ h}} $\end{document}) methods based on linear regression are available to assess hearing damage, the complexity of kurtosis effects necessitates introducing new metrics through nonlinear regression to improve predictive accuracy for hearing loss from non-steady noise exposure. …”
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Identification of Wiener Model with Internal Noise Using a Cubic Spline Approximation-Bayesian Composite Quantile Regression Algorithm by Tianhong Pan, Wei Guo, Ying Song, Fujia Yin

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Automatic Detection of Concrete Spalling Using Piecewise Linear Stochastic Gradient Descent Logistic Regression and Image Texture Analysis by Nhat-Duc Hoang, Quoc-Lam Nguyen, Xuan-Linh Tran

“…This improvement can enhance the capability of logistic regression in dealing with spall detection as a complex pattern classification problem. …”
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GB-SAR Engineering Interference Suppression Method Integrating Amplitude-Phase Feature Analysis and Robust Regression by Wenting Zhang, Tao Lai, Yuanhui Mo, Haifeng Huang, Qingsong Wang, Zhihua Zhou

“…Complex construction environments can interfere with ground-based synthetic aperture radar (GB-SAR) deformation monitoring, potentially leading to missed or false alarms. …”
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A Hybrid ARIMA-LSTM-XGBoost Model with Linear Regression Stacking for Transformer Oil Temperature Prediction by Xuemin Huang, Xiaoliang Zhuang, Fangyuan Tian, Zheng Niu, Yujie Chen, Qian Zhou, Chao Yuan

“…ARIMA captures linear components of the data, while LSTM models complex nonlinear dependencies. XGBoost is used to predict the overall oil temperature by learning from the complete dataset, effectively handling complex patterns. …”
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Modelling Geographically Weighted Truncated Spline Regression Using Maximum Likelihood Estimation for Human Development Disparities by Laode Muhammad Saris, Henny Pramoedyo, Adji Achmad Rinaldo Fernandes

“…A development of nonparametric truncated spline regression, Geographically Weighted Regression Spline Truncated (GWSTR) incorporates spatial effects in the modelling of nonlinear relationships between the response and predictor variables. …”
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Enhancing accuracy in modelling highly multicollinear data using alternative shrinkage parameters for ridge regression methods by Nadeem Akhtar, Muteb Faraj Alharthi

“…Abstract In this study, we introduce three new shrinkage parameters for ridge regression, which dynamically adjust the ridge penalty based on the properties of the data, particularly the multicollinearity structure. …”
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LinRegDroid: Detection of Android Malware Using Multiple Linear Regression Models-Based Classifiers by Durmus Ozkan Sahin, Sedat Akleylek, Erdal Kilic

“…As a result, remarkable performances are obtained with classification algorithms based on linear regression models without the need for very complex classification algorithms.…”
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Improving Generalization of Genetic Programming for High-Dimensional Symbolic Regression with Shapley Value Based Feature Selection by Chunyu Wang, Qi Chen, Bing Xue, Mengjie Zhang

“…Abstract Symbolic Regression (SR) on high-dimensional datasets often encounters significant challenges, resulting in models with poor generalization capabilities. …”
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Comparing the Performance of Regression and Machine Learning Models in Predicting the Usable Area of Houses with Multi-Pitched Roofs by Leszek Dawid, Anna Marta Barańska, Paweł Baran

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