Green gram yield prediction using linear regression by Tumusiime, Robert, Mabirizi, Vicent, Mirembe, D.P, Arinanye, T.R

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M Robust Weighted Ridge Estimator in Linear Regression Model by Taiwo Stephen Fayose, Kayode Ayinde, Olatayo Olusegun Alabi

Subjects: “…Linear regression model, Multicollinearity, M Estimator, Heteroscedasticity…”
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Error-Function-Based Penalized Quantile Regression in the Linear Mixed Model by Zelin Hang, Xiuli He

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Comparison of Linear Regression and Polynomial Regression for Predicting Rice Prices in Lhokseumawe City by Muhammad Iqbal, Rozzi Kesuma Dinata, Rizki Suwanda

“…The objective of this research is to develop a system for predicting the price of rice in Lhokseumawe City, employing a comparison of the accuracy of linear and polynomial regression models. To this end, daily price data from the Strategic Food Price Information Center (PIHPS) from 2020 to 2024 were utilized, with both models being implemented in Python. …”
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On the Upper Bounds of Test Statistics for a Single Outlier Test in Linear Regression Models by Tobias Ejiofor Ugah, Emmanuel Ikechukwu Mba, Micheal Chinonso Eze, Kingsley Chinedu Arum, Ifeoma Christy Mba, Henrietta Ebele Oranye

“…A bewildering large number of test statistics have been found for testing the presence of an outlier in multiple linear regression models. Exact critical values of these test statistics are not available, and approximate ones are usually obtained by the first-order Bonferroni upper bound or large-scale simulations. …”
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A versatile family of distributions: Log-linear regression model and applications to real data

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Dealing with the Outlier Problem in Multivariate Linear Regression Analysis Using the Hampel Filter by Amira Wali Omer, Taha Hussein Ali

“… Outliers in multivariate linear regression models can significantly distort parameter estimates, leading to biased results and reduced predictive accuracy. …”
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Estimating Models and Evaluating their Efficiency under Multicollinearity in Multiple Linear Regression: A Comparative Study by Saman Hussein Mahmood

“…The research aims to diagnose the multicollinearity problem between explanatory variables in the linear regression model and identifying the variables causing this problem based on the variance inflation factor (VIF), then estimation and evaluate the performance of three alternative methods, which are Ridge regression, principal components analysis, and Feedforward Neural Networks (FFNN) models with one and two hidden layers and application of the models to compressive strength data for high-performance concrete. …”
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Enhanced genetic fine mapping accuracy with Bayesian Linear Regression models in diverse genetic architectures. by Merina Shrestha, Zhonghao Bai, Tahereh Gholipourshahraki, Astrid J Hjelholt, Sile Hu, Mads Kjolby, Palle Duun Rohde, Peter Sørensen

“…We evaluated Bayesian Linear Regression (BLR) models with BayesC and BayesR priors as statistical genetic fine-mapping tools, comparing their performance to established methods such as FINEMAP and SuSiE. …”
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QSAR modeling of antimalarial activity of urea derivatives using genetic algorithm–multiple linear regressions by Abolghasem Beheshti, Eslam Pourbasheer, Mehdi Nekoei, Saadat Vahdani

“…Results showed that the predictive ability of the model was satisfactory, and it can be used for designing similar group of antimalarial compounds.…”
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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

“…The predictions of these three models are combined through a linear-regression stacking approach, improving accuracy and simplifying the model structure. …”
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Computing Parameter Estimates of a Homogeneous Nested Piecewise Linear Regression by S. I. Noskov, S. I. Belinskaya

“…Result. The generated linear programming problem has an acceptable dimension for solving practical modeling problems. …”
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Risk Comparison of Improved Estimators in a Linear Regression Model with Multivariate t Errors under Balanced Loss Function by Guikai Hu, Qingguo Li, Shenghua Yu

“…Under a balanced loss function, we derive the explicit formulae of the risk of the Stein-rule (SR) estimator, the positive-part Stein-rule (PSR) estimator, the feasible minimum mean squared error (FMMSE) estimator, and the adjusted feasible minimum mean squared error (AFMMSE) estimator in a linear regression model with multivariate t errors. …”
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Combined application of methods of maximum consistency and anti-robust parameter estimation in the construction of regression models by S. I. Noskov, Yu. A. Bychkov

“…The final choice of parameter values remains with the model developer.…”
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On the Stochastic Restricted r-k Class Estimator and Stochastic Restricted r-d Class Estimator in Linear Regression Model by Jibo Wu

“…The stochastic restricted r-k class estimator and stochastic restricted r-d class estimator are proposed for the vector of parameters in a multiple linear regression model with stochastic linear restrictions. …”
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ESTIMATION OF HEALTHY AND LIVER DISEASED INDIVIDUALS BY A LINEAR REGRESSION CLASSIFICATION ALGORITHM by Handan Tanyıldızı Kökkülünk

“…Machine learning was improved by selecting parameters that have a high contribution to the prediction by using the Akaike information criterion.Results: The three strongest parameters with a positive effect on the estimation were AST, BIL, and GGT, respectively; The three strongest parameters with negative effects were CHOL, CHE, and ALB, respectively. The accuracy of the model used was 91%, the precision was 99%, the recall was 0.91, and the F score was 94%. …”
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Analysing the nexus between income inequality and economic growth in Ethiopia: using the non linear auto regressive distributed lag model by Tadesse Wudu Abate, Kalid Wendimnew Sitotaw

“…The estimation result from the ‘non linear’ ARDL model shows variables that can affect the level of the Gini index in both the short run and long run periods. …”
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Regularized regression outperforms trees for predicting cognitive function in the Health and Retirement Study by Kyle Masato Ishikawa, Deborah Taira, Joseph Keaweʻaimoku Kaholokula, Matthew Uechi, James Davis, Eunjung Lim

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Multiple linear regression parameters for determining fatigue-based entropy characterisation of magnesium alloy by Mohd 'Akashah Fauthan, Shahrum Abdullah, Mohamad Faizal Abdullah, Intan Fadhlina Mohamed

“…The assumptions of the models were considered through graphical residual analysis. …”
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Non-Linear Models for Assessing Soil Moisture Estimation by Rui Li, Susu Wang, Han Wu, Hao Dong, Dezhi Kong, Hanxue Li, Dorothy S. Zhang, Haitao Chen

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