The reciprocal Bayesian LASSO

Mallick, Himel; Alhamzawi, Rahim; Paul, Erina; Svetnik, Vladimir

doi:10.1002/sim.9098

Cited by 12 publications

(4 citation statements)

References 52 publications

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“…However, previous evidence has confirmed that stepwise selection could generate the risk of model overfitting [ 50 ]. Modern statistical methods, such as bootstrapping or the least absolute shrinkage and selection operator, are promising methods for identifying important variables to resolve the overfitting problem [ 51 , 52 ]. Therefore, future studies should restrict the candidate list and adopt the shrinkage method to develop high-quality prediction models.…”

Section: Discussionmentioning

confidence: 99%

Clinical prediction models for the early diagnosis of obstructive sleep apnea in stroke patients: a systematic review

Yang,

Lu,

Yang

2024

Syst Rev

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Background Obstructive sleep apnea (OSA) is a common sleep disorder characterized by repetitive cessation or reduction in airflow during sleep. Stroke patients have a higher risk of OSA, which can worsen their cognitive and functional disabilities, prolong their hospitalization, and increase their mortality rates. Methods We conducted a comprehensive literature search in the databases of PubMed, CINAHL, Embase, PsycINFO, Cochrane Library, and CNKI, using a combination of keywords and MeSH words in both English and Chinese. Studies published up to March 1, 2022, which reported the development and/or validation of clinical prediction models for OSA diagnosis in stroke patients. Results We identified 11 studies that met our inclusion criteria. Most of the studies used logistic regression models and machine learning approaches to predict the incidence of OSA in stroke patients. The most frequently selected predictors included body mass index, sex, neck circumference, snoring, and blood pressure. However, the predictive performance of these models ranged from poor to moderate, with the area under the receiver operating characteristic curve varying from 0.55 to 0.82. All the studies have a high overall risk of bias, mainly due to the small sample size and lack of external validation. Conclusion Although clinical prediction models have shown the potential for diagnosing OSA in stroke patients, their limited accuracy and high risk of bias restrict their implications. Future studies should focus on developing advanced algorithms that incorporate more predictors from larger and representative samples and externally validating their performance to enhance their clinical applicability and accuracy.

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Section: Discussionmentioning

confidence: 99%

Clinical prediction models for the early diagnosis of obstructive sleep apnea in stroke patients: a systematic review

Yang,

Lu,

Yang

2024

Syst Rev

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“…The researcher [11] Mallick and others proposed in the year 2021 a proposal to represent the previous (prior) distribution of the parameter β, as this proposal assumed that the inverse Laplace distribution of the parameter β can be represented as follows:…”

Section: Iii3 Binary Reciprocal Lasso Quantile Regressionmentioning

confidence: 99%

“…Where it can be said that the relationship (3)(4)(5)(6)(7)(8)(9)(10)(11) produced the distribution of the variable λ to be .…”

Section: Iii5 the Full Conditional Posterior Distributionmentioning

confidence: 99%

Bayesian Binary reciprocal LASSO quantile regression (with practical application)

Al-mayali

2023

Jour. Kufa Math. Comp.

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Quantile regression is one of the methods that has taken a wide space in application in the previous two decades because of the attractive features of these methods to researchers, as it is not affected by outliers values, meaning that it is considered one of the robust methods, and it gives more details of the effect of explanatory variables on the dependent variable.In this paper, a Bayesian hierarchical model for variable selection and estimation in the context of binary quantile regression is proposed. Current approaches to variable selection in the context of binary classification are sensitive to outliers, heterogeneous values, and other anomalies. The proposed method in this study overcomes these problems in an attractive and direct way.

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“…Mallick et al (2021) and Zainab and Alhamzawi (2022), the above Bayesian hierarchical modelling produce an efficient Gibbs sampler that works as follows.…”

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confidence: 99%

Bayesian reciprocal bridge composite tobit quantile regression

Alsaadi

Alhamzawi

2022

ijhs

Self Cite

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A composite tobit quantile regression approach is proposed for Bayesian simultaneous covariate selection and estimation in the setting of left censored regression. The proposed approach uses prior distributions for the regression coefficients that are scale mixtures of inverse uniform priors on the coefficients and independent Gamma priors on their mixing parameters. The proposed method was illustrated using simulation examples. Results show that the proposed method performs very well compared to the existing method.

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The reciprocal Bayesian LASSO

Cited by 12 publications

References 52 publications

Clinical prediction models for the early diagnosis of obstructive sleep apnea in stroke patients: a systematic review

Clinical prediction models for the early diagnosis of obstructive sleep apnea in stroke patients: a systematic review

Bayesian Binary reciprocal LASSO quantile regression (with practical application)

Bayesian reciprocal bridge composite tobit quantile regression

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