Bayesian Adaptive Lasso for the Partial Functional Linear Spatial Autoregressive Model
Journal of Mathematics, 2022 This study introduces a partial functional linear spatial autoregressive model which can explore the relationship between a scalar spatially dependent response variable and predictive variables containing both multiple scalar covariates and a functional ...
Dengke Xu, Ruiqin Tian, Ying Lu
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Indoor WLAN localization via adaptive Lasso Bayesian inference and convex optimization [PDF]
Cogent Engineering, 2020 There has been growing interest in indoor positioning system technologies due to the important role of real-time indoor positioning services in modern technologies such as security services and emergency healthcare.
Osamah A. Abdullah +2 more
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Bayesian Adaptive Lasso for Regression Models with Nonignorable Missing Responses [PDF]
Journal of Mathematics, 2022 The main purpose of this article is to develop a Bayesian adaptive lasso procedure for analyzing linear regression models with nonignorable missing responses, in which the missingness mechanism is specified by a logistic regression model.
Yuanying Zhao, Xingde Duan
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Regularized regression models for accurate prediction of HIV progression in ART patients: a comparative study [PDF]
Scientific ReportsThis study compared four regularized regression techniques Ridge, Least Absolute Shrinkage and Selection Operator (LASSO), Adaptive LASSO, and Elastic Net to analyze survival models for HIV progression.
Grace O. Owoade +3 more
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Bayesian Elastic Net Cox Models for Time-to-Event Prediction: Application to a Breast Cancer Cohort [PDF]
EntropyHigh-dimensional survival analyses require calibrated risk and measurable uncertainty, but standard elastic net Cox models provide only point estimates.
Ersin Yılmaz +2 more
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Selecting predictive biomarkers from genomic data.
PLoS ONE, 2022 Recently there have been tremendous efforts to develop statistical procedures which allow to determine subgroups of patients for which certain treatments are effective. This article focuses on the selection of prognostic and predictive genetic biomarkers
Florian Frommlet +3 more
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Modified Quantile Regression for Modeling the Low Birth Weight
Frontiers in Applied Mathematics and Statistics, 2022 This study aims to identify the best model of low birth weight by applying and comparing several methods based on the quantile regression method's modification.
Ferra Yanuar +2 more
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Empowering differential networks using Bayesian analysis.
PLoS ONE, 2022 Differential networks (DN) are important tools for modeling the changes in conditional dependencies between multiple samples. A Bayesian approach for estimating DNs, from the classical viewpoint, is introduced with a computationally efficient threshold ...
Jarod Smith +2 more
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New adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection
BMC Medical Research Methodology, 2021 Background Adverse effects of drugs are often identified after market introduction. Post-marketing pharmacovigilance aims to detect them as early as possible and relies on spontaneous reporting systems collecting suspicious cases.
Émeline Courtois +2 more
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PLoS ONE, 2020 This paper presents a Bayesian analysis of linear mixed models for quantile regression based on a Cholesky decomposition for the covariance matrix of random effects.
Yonggang Ji, Haifang Shi
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