Differential diagnosis of viral and bacterial community-acquired pneumonia in children using logistic regression by E. A. Kozyrev, S. G. Grigor’ev, I. V. Babachenko, A. V. Orlov, E. A. Martens, E. V. Nikitina, E. V. Aleksandrova, N. V. Marchenko, D. Yu. Novokshonov, E. D. Orlova

“…Conclusion. The regression model based on a complex of clinical (age, BOS) and laboratory signs (ANC, Band, PDW) has high statistical significance (p<0.001) and excellent diagnostic ability (84.2%) and can be used for early differential diagnosis viral and bacterial pediatric CAP in different health care settings.…”
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Explicit Model for Chiller Fault Diagnosis Based on Multi-objective Regression with Different Weights by Wu Kongrui, Han Hua, Yang Yuting, Lu Hailong, Ling Minbin

“…The weighted regression model was slightly more complex than the pure linear regression model; however, the fault diagnosis performance was clearly better, and the minimum performance was improved by 40.50% under different feature sets. …”
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A Unit Weibull Loss Distribution with Quantile Regression and Practical Applications to Actuarial Science by Abdul Ghaniyyu Abubakari, Suleman Nasiru, Christophe Chesneau

“…This makes the new distribution suitable for modeling data with complex characteristics. Statistical properties such as the quantile, moments, and moment-generating function are determined. …”
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Quantum machine learning regression optimisation for full-scale sewage sludge anaerobic digestion by Yomna Mohamed, Ahmed Elghadban, Hei I Lei, Amelie Andrea Shih, Po-Heng Lee

“…However, its efficiency improvement is hindered by complex microbial communities and sensitivity to feedstock properties. …”
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Multiple linear regression model was constructed based on the influencing factors of vancomycin trough concentration by Lin Wang, Yuhuang Wang, Chunyan Yang, Jia Jiang, Huifang Wang, Mingcai Wu

“…Conclusion: The influencing factors of Vancomycin trough concentration in blood were complex. The constructed regression model of Vancomycin concentration in plasma may provide a scientific reference for individualized administration of Vancomycin in clinical practice.…”
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The Estimating Parameter and Number of Knots for Nonparametric Regression Methods in Modelling Time Series Data by Autcha Araveeporn

“…This research aims to explore and compare several nonparametric regression techniques, including smoothing splines, natural cubic splines, B-splines, and penalized spline methods. …”
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High-Sensitivity Electrical Admittance Sensor with Regression Analysis for Measuring Mixed Electrolyte Concentrations by Chun-Chi Chen, Chih-Hung Hung, Han-Xiang Zhu, Ji-Zun Chen

“…The sensor device requires no complex operational procedures and can quickly complete measurements, making it well-suited for point-of-care applications. …”
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KNOT OPTIMIZATION FOR BI-RESPONSE SPLINE NONPARAMETRIC REGRESSION WITH GENERALIZED CROSS-VALIDATION (GCV) by Andre Fajry Al Barra, Dewi Retno Sari Saputro

“…Nonparametric regression models are flexible and can capture complex relationships that may not be adequately represented by simple parametric forms. …”
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Cointegration and Regression Analyses as Alternative Methods to Verify the Protective Properties of Inflation Hedge Investments by Rafal Wolski

“…Implications: The study enables a better understanding of the complex dynamics involved in preserving purchasing power during periods of high inflation. …”
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A hybrid approach combining Bayesian networks and logistic regression for enhancing risk assessment by Xueyuan Wei, Yingdong Dong

“…First, a probabilistic causal model is built as a BN to capture complex interdependencies among vulnerability characteristics such as CVSS score, exploit complexity, and attack vector. …”
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Investigation of an Optimized Linear Regression Model with Nonlinear Error Compensation for Tool Wear Prediction by Lihua Shen, Baorui Du, He Fan, Hailong Yang

“…By combining linear modeling with nonlinear error compensation, this method provides an integrated optimization approach to prediction tasks in complex industrial scenarios.…”
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New two parameter hybrid estimator for zero inflated negative binomial regression models by Fatimah A. Almulhim, M. Nagy, Ali T. Hammad, A. H. Mansi, Getachew Tekle Mekiso, M. M. Abd El-Raouf

“…Abstract The zero-inflated negative binomial regression (ZINBR) model is used for modeling count data that exhibit both overdispersion and zero-inflated counts. …”
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Prediction of chicken gender before putting eggs in incubator using logistic regression model by Abbas Parchami, Ramin Aghaei Afshar, Erfan Fadaie, Hasan Ghaemi, Ehsanollah Sakhaee

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Investigating the Determinants of Toxoplasma gondii Prevalence in Meat: A Systematic Review and Meta-Regression. by Simone Belluco, Marzia Mancin, Daniele Conficoni, Giulia Simonato, Mario Pietrobelli, Antonia Ricci

“…However, its role in healthy people is probably under-appreciated. The complex epidemiology of this protozoan recognizes several infection routes but consumption of contaminated food is likely to be the predominant one. …”
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Estimation of soil thickness in karst landforms using a quantile regression forests approach by Fayong Fang, Ruyi Zi, Zhen Han, Qian Fang, Rui Hou, Longshan Zhao

“…We employed a quantile regression forests (QRF) approach to estimate soil thickness and evaluate the associated uncertainty in the predicted results. …”
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Multi-Output Regression for the Prediction of World-Class Performances in Women’s Handball by Rayane Elimam, Nicolas NICOLAS, Jacques Prioux, Jacky Montmain, Stephane Perrey

“…We compared 4 single-output models (kNN, regression tree, random forest and(NN) Predictive models inspired by the human brain, used in this study for multi-output prediction in sports performance analysis (neural networks)), their multi-output counterparts and aA baseline model predicting future performance as the average of each player’s past performance, serving as a simple reference for comparison with more complex models (dummy baseline) (predicting the average performance of each player over the last month) in terms of average(Root Mean Squared Error) A measure of the quadratic difference between predicted and actual values in regression models (RMSE) (aRMSE) during aAn evaluation method where past training and game data are used sequentially to predict performance of the next game (chronological evaluation) where previous trainings and games data are used to train models to predict the next game performances. …”
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Spatial analysis of mangrove ecosystem dynamics in Banyuwangi: a geographically weighted regression approach by Yulizar Ihrami Rahmila, Lilik Budi Prasetyo, Cecep Kusmana, Suyadi, Mohammad Basyuni, Bono Pranoto, Rinny Rahmania, Wawan Halwany, Varenna Faubiany, Tri Muji Susantoro, Gatot Winarso, Lisna Efiyanti, Dian Anggraini Indrawan

“…This study examines the dynamics of mangrove land change in Banyuwangi, East Java, employing Geographically Weighted Regression (GWR) to investigate the spatial variability in the drivers of mangrove deforestation. …”
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Regression Modeling Using Generalized Pareto-IV Distribution With Application to Household Budget Survey by January Ponera, Srinivasa Rao Gadde, Ismail Abbas

“…The study introduced the new regression model by the T-X family technique of Type I half-logistic and Pareto IV distribution (TIHLPIV) that had to solve the weaknesses of mixed methods, which are limited to applicability, data requirements, and computational complexity. …”
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Weighted mixed regression localization method based on three-dimensional Voronoi diagram division by Fenfang LI, Xiaochao DANG, Zhanjun HAO

“…With the development of the wireless communication technology and sensing technology, various technologies based on wireless sensor networks are applied.These technologies are widely used in the fields of intelligent agriculture, intelligent transportation, fire rescue and so on.Node localization technology is one of the basic technologies of wireless sensor networks.Location information is a part of the sensing data, which determines the specific measures to be taken in the next step.Due to the complexity of the three-dimensional (3D) space localization environment, the application of the plane positioning method in 3D space will have some limitations.Aiming at above problems, the weighted hybrid regression location algorithm WMR-SKR based on a 3D Voronoi diagram was studied.The localization algorithm was divided into two stages: offline training and online testing.The 3D space was divided into Voronoi diagrams according to the anchor nodes in the network.In the offline training stage, the sequence composed of the coordinates of the anchor nodes and Voronoi cell vertices was used as the training set for training.In the online test stage, the coordinates of unknown nodes in the network were predicted through the trained localization model.Simulation results show that the WMR-SKR algorithm can effectively reduce the node localization error and improve the node localization speed in 3D space.…”
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Twin Support Vector Regression Model Based on Heteroscedastic Gaussian Noise and Its Application by Shiguang Zhang, Ge Feng, Feng Yuan, Shuangle Guo

“…Therefore, TSVR not only has the advantages of fast computation and low computational complexity, but also has better regression performance. …”
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