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Estimating influenza incidence using search query deceptiveness and generalized ridge regression. [PDF]

PLoS Computational Biology, 2019
Seasonal influenza is a sometimes surprisingly impactful disease, causing thousands of deaths per year along with much additional morbidity. Timely knowledge of the outbreak state is valuable for managing an effective response.
Reid Priedhorsky, Ashlynn R Daughton, Martha Barnard, Fiona O'Connell, Dave Osthus   +4 more
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Development of the generalized ridge estimator for the Poisson-Inverse Gaussian regression model with multicollinearity [PDF]

Scientific Reports
The Poisson-Inverse Gaussian regression model is a widely used method for analyzing count data, particularly in over-dispersion. However, the reliability of parameter estimates obtained through maximum likelihood estimation in this model can be ...
Fatimah A. Almulhim, Ali T. Hammad, M. E. Bakr, Oluwafemi Samson Balogun, Alexis Habineza, M. M. Abd El-Raouf   +5 more
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A bias-reduced estimator for generalized Poisson regression with application to carbon dioxide emission in Canada [PDF]

Scientific Reports
The generalized Poisson regression model (GPRM) provides a flexible framework for modeling count data, especially those exhibiting over- or underdispersion.
Fatimah M. Alghamdi, Ahmed M. Gemeay, Gamal A. Abd-Elmougod, Ehab M. Almetwally, M. A. El-Qurashi, Getachew Tekle Mekiso, Ali T. Hammad   +6 more
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Forecasting water quality indices using generalized ridge model, regularized weighted kernel ridge model, and optimized multivariate variational mode decomposition [PDF]

Scientific Reports
Permeability index (PI) and magnesium absorption ratio (MAR) are both primary irrigation water quality indicators (IWQI) used to evaluate the efficacy of agricultural water supplies.
Marjan Kordani, Mohsen Bagheritabar, Iman Ahmadianfar, Arvin Samadi-Koucheksaraee   +3 more
doaj   +2 more sources

Regularization Paths for Generalized Linear Models via Coordinate Descent [PDF]

Journal of Statistical Software, 2010
We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multi- nomial regression problems while the penalties include ℓ1 (the lasso), ℓ2 (ridge
Jerome Friedman, Trevor Hastie, Rob Tibshirani   +2 more
doaj   +1 more source

Mapping the EORTC QLQ-C30 and QLQ-LC13 to the SF-6D utility index in patients with lung cancer using machine learning and traditional regression methods [PDF]

Health and Quality of Life Outcomes
Background Preference-based measures of health-related quality of life (HRQoL), such as the Short Form Six-Dimension (SF-6D) is essential for health economic evaluations.
Longlin Jiang, Kexun Li, Simiao Lu, Zhou Hong, Yifang Wang, Qin Xie, Qin He, Sirui Wei, Aoru Zhou, Hong Kang, Xuefeng Leng, Qing Yang, Yan Miao   +12 more
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Optimizing genomic selection models for wheat breeding under contrasting water regimes in a mediterranean environment [PDF]

Plant Methods
Background Bread wheat (Triticum aestivum L.) is a vital global crop, supplying 20% of the protein in the human diet. Improving its productivity and resilience, particularly under water-limited conditions, is a major breeding priority.
Venkata Rami Reddy Yannam, Marta S. Lopes, Jose Miguel Soriano   +2 more
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Ridge Regression and the Elastic Net: How Do They Do as Finders of True Regressors and Their Coefficients?

Mathematics, 2022
For the linear model Y=Xb+error, where the number of regressors (p) exceeds the number of observations (n), the Elastic Net (EN) was proposed, in 2005, to estimate b.
Rajaram Gana
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Ridge regression and its applications in genetic studies.

PLoS ONE, 2021
With the advancement of technology, analysis of large-scale data of gene expression is feasible and has become very popular in the era of machine learning. This paper develops an improved ridge approach for the genome regression modeling.
M Arashi, M Roozbeh, N A Hamzah, M Gasparini   +3 more
doaj   +1 more source

Kibria-Lukman Estimator for General Linear Regression Model with AR(2) Errors: A Comparative Study with Monte Carlo Simulation

Journal of New Theory, 2022
The sensitivity of the least-squares estimation in a regression model is impacted by multicollinearity and autocorrelation problems. To deal with the multicollinearity, Ridge, Liu, and Ridge-type biased estimators have been presented in the statistical ...
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doaj   +1 more source