Results 21 to 30 of about 1,681,604 (218)
Study of Bayesian variable selection method on mixed linear regression models.
PLoS ONE, 2023 Variable selection has always been an important issue in statistics. When a linear regression model is used to fit data, selecting appropriate explanatory variables that strongly impact the response variables has a significant effect on the model ...
Yong Li, Hefei Liu, Rubing Li
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What Drives Financial Sector Development in Africa? Insights from Machine Learning
Applied Artificial Intelligence, 2021 This study uses machine learning techniques to identify the key drivers of financial development in Africa. To this end, four regularization techniques – the Standard lasso, Adaptive lasso, the minimum Schwarz Bayesian information criterion lasso, and ...
Isaac K. Ofori +2 more
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The prediction of live weight of hair goats through penalized regression methods: LASSO and adaptive LASSO [PDF]
Archives Animal Breeding, 2018 The least absolute selection and shrinkage operator (LASSO) and adaptive LASSO methods have become a popular model in the last decade, especially for data with a multicollinearity problem.
S. Akkol
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Penalized flexible Bayesian quantile regression [PDF]
, 2012 Copyright © 2012 SciResThis article has been made available through the Brunel Open Access Publishing Fund.The selection of predictors plays a crucial role in building a multiple regression model. Indeed, the choice of a suitable subset of predictors can
Yu, K, Alkenani, A, Alhamzawi, R
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Bayesian Regularisation in Structured Additive Regression Models for Survival Data [PDF]
, 2008 During recent years, penalized likelihood approaches have attracted a lot of interest both in the area of semiparametric regression and for the regularization of high-dimensional regression models.
Konrath, Susanne +2 more
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IET Signal Processing, 2023 Due to its self‐regularising nature and its ability to quantify uncertainty, the Bayesian approach has achieved excellent recovery performance across a wide range of sparse signal recovery applications.
Zonglong Bai +3 more
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An Integrated Artificial Intelligence Framework for Nanomedicine Engineering. [PDF]
Adv Sci (Weinh)This review outlines a closed‐loop AI ecosystem for nanomedicine engineering. By synergizing multimodal data harmonization, objective‐driven AI architectures, and automated high‐throughput validation, this “Design‐Make‐Test‐Analyze” (DMTA) workflow transforms traditional heuristic formulation into a predictive, self‐optimizing paradigm, accelerating ...
Dong M +10 more
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Journal of Ocean Engineering and Science, 2022 An accurate prediction of ship fuel consumption is critical for speed, trim, and voyage optimisation etc. While previous studies have focused on predicting ship fuel consumption with respect to a variety of factors, research on the impact of ...
Tianrui Zhou +3 more
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Robust Bayesian Regularized Estimation Based on t Regression Model
Journal of Probability and Statistics, 2015 The t distribution is a useful extension of the normal distribution, which can be used for statistical modeling of data sets with heavy tails, and provides robust estimation. In this paper, in view of the advantages of Bayesian analysis, we propose a new
Zean Li, Weihua Zhao
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Bayesian adaptive Lasso Tobit regression
Journal of Al-Qadisiyah for Computer Science and Mathematics, 2019 In this paper, we introduce a new procedure for model selection in Tobit regression, we suggest the Bayesian adaptive Lasso Tobit regression (BALTR) for variable selection (VS) and coefficient estimation. We submitted a Bayesian hierarchical model and Gibbs sampler (GS) for our procedure.
Haider Kadhim Abbas, Rahim Jabbar Thaher
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