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September 10, 2025Periodicals of Engineering and Natural Sciences (PEN)Open Access

Bayesian group Lasso regression for left-censored data

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Authors

SASaja Hussein AljanabiUniversity of Al-Qadisiyah RARahim AlhamzawiUniversity of Al-Qadisiyah

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Implication

This approach applies Bayesian group lasso for variable selection and coefficient estimation in left-censored data, showing better performance than existing methods.

Key Points

The proposed Bayesian group Lasso regression effectively addresses challenges in left-censored data.
Simulation studies reveal that the new method significantly outperforms traditional approaches.
A Gibbs sampler is employed for sampling parameters from the posterior distribution.
The hierarchical Bayesian modeling introduces a novel framework for tackling variable selection.

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Aljanabi et al. (2020) studied this question.

synapsesocial.com/papers/68c19aad9b7b07f3a061c528 https://doi.org/10.21533/pen.v8.i2.1083

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Implication

This approach applies Bayesian group lasso for variable selection and coefficient estimation in left-censored data, showing better performance than existing methods.

Bayesian group Lasso regression for left-censored data

Discussion

Member takes

Implication

Key Points

Cite This Study

Bayesian group Lasso regression for left-censored data

Discussion

Member takes

Implication

Key Points

Cite This Study

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