On identification methods of linear regression models by Romualdas Salėtis

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Smoothing Estimation of Parameters in Censored Quantile Linear Regression Model by Mingquan Wang, Xiaohua Ma, Xinrui Wang, Jun Wang, Xiuqing Zhou, Qibing Gao

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Modified One-Parameter Liu Estimator for the Linear Regression Model by Adewale F. Lukman, B. M. Golam Kibria, Kayode Ayinde, Segun L. Jegede

“…Motivated by the ridge regression (Hoerl and Kennard, 1970) and Liu (1993) estimators, this paper proposes a modified Liu estimator to solve the multicollinearity problem for the linear regression model. This modification places this estimator in the class of the ridge and Liu estimators with a single biasing parameter. …”
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Properties of the coefficient estimators for the linear regression model with heteroskedastic error term by Alfredas Račkauskas, Danas Zuokas

“… In this paper we present estimated generalized least squares (EGLS) estimator for the coefficient vector β in the linear regression model y = βX + ε, where disturbance term can be heteroskedastic. …”
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Performance of Some Stochastic Restricted Ridge Estimator in Linear Regression Model by Jibo Wu, Chaolin Liu

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Variables related to socialanxiety in adolescents: a multiple linear regression model by Antonio Samaniego Pinho, Marcelo Buenahora Bernal

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A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications by B. M. Golam Kibria, Adewale F. Lukman

“…This paper proposes a new estimator to solve the multicollinearity problem for the linear regression model. Theory and simulation results show that, under some conditions, it performs better than both Liu and ridge regression estimators in the smaller MSE sense. …”
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Least Absolute Deviation Estimate for Functional Coefficient Partially Linear Regression Models by Yanqin Feng, Guoxin Zuo, Li Liu

“…The functional coefficient partially linear regression model is a useful generalization of the nonparametric model, partial linear model, and varying coefficient model. …”
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Multiple Linear Regression Model Based on Neural Network and Its Application in the MBR Simulation by Chunqing Li, Zixiang Yang, Yiquan Deng, Tao Wang

“…The system is researched and developed with the hybrid programming of VC++ programming language and OpenGL, with a multifactor linear regression model of affecting MBR membrane fluxes based on neural network, applying modeling method of integer instead of float and quad tree recursion. …”
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Correction of TRMM 3B42V7 Based on Linear Regression Models over China by Shaohua Liu, Denghua Yan, Tianling Qin, Baisha Weng, Meng Li

“…In this paper, linear regression models (LRMs) have been constructed to correct and downscale the TRMM precipitation based on the gauge precipitation at 2257 stations over China from 1998 to 2013. …”
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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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Variation Comparison of OLS and GLS Estimators using Monte Carlo Simulation of Linear Regression Model with Autoregressive Scheme by Sajid AliKhan, Sayyad Khurshid, Tooba Akhtar, Kashmala Khurshid

“… In this research we discusses to Ordinary Least Squares and Generalized Least Squares techniques and estimate with First Order Autoregressive scheme from different correlation levels by using simple linear regression model. A comparison has been made between these two methods on the basis of variances results. …”
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Uncertainty analysis of linear and non-linear regression models in the modeling of water quality in the Caspian Sea basin: Application of Monte-Carlo method by Sahar Heidari Masteali, Mahmoud Bayat, Pete Bettinger, Mansour Ghorbanpour

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Improving Regional Dynamic Downscaling with Multiple Linear Regression Model Using Components Principal Analysis: Precipitation over Amazon and Northeast Brazil by Aline Gomes da Silva, Claudio Moises Santos e Silva

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Use of Artificial Neural Networks and Multiple Linear Regression Model for the Prediction of Dissolved Oxygen in Rivers: Case Study of Hydrographic Basin of River Nyando, Kenya by Yashon O. Ouma, Clinton O. Okuku, Evalyne N. Njau

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ϵ-Confidence Approximately Correct (ϵ-CoAC) Learnability and Hyperparameter Selection in Linear Regression Modeling by Soosan Beheshti, Mahdi Shamsi

“…The hypothesis is chosen from classes with potentially different complexities. Linear regression modeling is an important category of learning algorithms. …”
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On QSPR analysis of pulmonary cancer drugs using python-driven topological modeling by Huiling Qin, Mazhar Hussain, Muhammad Farhan Hanif, Muhammad Kamran Siddiqui, Zahid Hussain, Mohamed Abubakar Fiidow

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Genetic interaction between oxidative stress and body mass index in a Spanish population by Francisco Lara-Hernández, Rebeca Melero, María Elena Quiroz-Rodríguez, Celeste Moya-Valera, Mariana de Jesús Gallardo-Espinoza, Luis Álvarez, Ingrid Lizeth Valarezo-Torres, Laisa Briongos-Figuero, Jessica Abadía-Otero, Francisco Javier Mena-Martin, Guillermo Saez, Josep Redon, Juan-Carlos Martín-Escudero, Ana-Bárbara García-García, Guillermo Ayala, Felipe Javier Chaves

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Simulation of Dissolved Oxygen Concentration in an Urban Lake Based on MLR and ARDL by ZHAO Hongcheng, YANG Fei, ZHOU Peng, GUO Jiacheng, HUANG Jinbai

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Examining spatiotemporal dynamics of CO2 emission at multiscale based on nighttime light data by Binbin Zhang, Zongzheng Liang, Wenru Guo, Zhanyou Cui, Deguang Li

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