Hierarchical Bayesian adaptive lasso methods on exponential random graph models
Applied Network Science, 2023 The analysis of network data has become an increasingly prominent and demanding field across multiple research fields including data science, health, and social sciences, requiring the development of robust models and efficient computational methods. One
Dan Han +3 more
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Bayesian extensions on Lasso and adaptive Lasso Tobit regressions [PDF]
Periodicals of Engineering and Natural Sciences (PEN), 2020 Since lasso method launched, a lot of applications and extensions were run on it which made it to become deeply widely used in various discipline. In this paper, we proposed the Scale Mixture of Normals mixing with Rayleigh (SMNR) distribution on their ...
Flaih, Ahmad Naeem +2 more
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New penalized Bayesian adaptive lasso binary regression [PDF]
Periodicals of Engineering and Natural Sciences (PEN), 2021 The scale mixture of normal mixing with Rayleigh as representation of Laplace prior of has introduced by Flaih et al. [1]. We employed this new scale mixture for the adaptive lasso Binary regression.
Al-rubaye, Ali Abdulmohsin Abdulraeem +1 more
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Bayesian adaptive lassos with non-convex penalization [PDF]
, 2007 The lasso (Tibshirani,1996) has sparked interest in the use of penalization of the log-likelihood for variable selection, as well as shrinkage. Recently, there have been attempts to propose penalty functions which improve upon the Lassos properties ...
Griffin, Jim E., Brown, Philip J.
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BAYESIAN ANALYSIS OF TOBIT QUANTILE REGRESSION WITH ADAPTIVE LASSO PENALTY IN HOUSEHOLD EXPENDITURE FOR CIGARETTE CONSUMPTION [PDF]
Jurnal Statistika Universitas Muhammadiyah Semarang, 2023 Tobit Quantile Regression with Adaptive Lasso Penalty is a quantile regression model on censored data that adds Lasso's adaptive penalty to its parameter estimation. The estimation of the regression parameters is solved by Bayesian analysis.
Rahmawati, Fitri, Subanar, Subanar
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Adaptive Shrinkage with a Nonparametric Bayesian Lasso
Journal of Computational and Graphical StatisticsModern approaches to perform Bayesian variable selection rely mostly on the use of shrinkage priors. That said, an ideal shrinkage prior should be adaptive to different signal levels, ensuring that small effects are ruled out, while keeping relatively ...
Loong, Bronwyn +2 more
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MODEL LAMA RAWAT INAP PASIEN COVID-19 MENGGUNAKAN REGRESI KUANTIL BAYESIAN DENGAN METODE LASSO DAN ADAPTIVE LASSO [PDF]
, 2022 Penelitian ini bertujuan untuk mengidentifikasi model terbaik dari data lama rawat inap pasien COVID-19 di Provinsi Sumatera Barat dengan menggunakan metode regresi kuantil Bayesian LASSO dan Adaptive LASSO.
Athifa, Salsabila Deva
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Prior elicitation and variable selection for bayesian quantile regression [PDF]
, 2013 This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Bayesian subset selection suffers from three important difficulties: assigning priors over model space, assigning priors to all components of the regression
Al-Hamzawi, Rahim Jabbar Thaher
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Prediction of Prevalent Cardiovascular Disease Using Social, Environmental, and Behavioral Factors: A Vital Conditions Framework Approach. [PDF]
Chronic Dis Transl MedThe Vital Conditions Framework. Graphical representation of the Vital Conditions for Health Framework with seven domains. Images used with permission from The Rippel Foundation: An Introduction to the Vital Conditions for Health and Well‐Being. https://rippel.org/events/webinar-an-introduction-to-the-vital-conditions-for-health-and-well-being/. Federal
Kjelstrom S +4 more
europepmc +2 more sources
Bayesian adaptive group lasso with semiparametric hidden Markov models [PDF]
Statistics in Medicine, 2018 This paper presents a Bayesian adaptive group least absolute shrinkage and selection operator method to conduct simultaneous model selection and estimation under semiparametric hidden Markov models. We specify the conditional regression model and the transition probability model in the hidden Markov model into additive nonparametric functions of ...
Kai Kang +3 more
openaire +2 more sources