ROC Analyses Based on Measuring Evidence Using the Relative Belief Ratio

Al-Labadi, Luai; Evans, Michael; Liang, Qiaoyu

doi:10.3390/e24121710

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…Additionally, this strategy does not require computationally burdensome calculations like Monte Carlo Markov chain (MCMC) methods that were required in the first strategy to compute marginal likelihoods. A comprehensive study that explains why the RB ratio is a more appropriate measure of evidence than the Bayes factor can also be found in Al-Labadi et al (2023).…”

Section: Previous Workmentioning

confidence: 99%

A Semi-Bayesian Nonparametric Hypothesis Test Using Maximum Mean Discrepancy with Applications in Generative Adversarial Networks

Fazeli-Asl¹,

Zhang²,

Lin³

2023

Preprint

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A classic inferential problem in statistics is the two-sample hypothesis test, where we test whether two samples of observations are either drawn from the same distribution or two distinct distributions. However, standard methods for performing this test require strong distributional assumptions on the two samples of data. We propose a semi-Bayesian nonparametric (semi-BNP) procedure for the two-sample hypothesis testing problem. First, we will derive a novel BNP maximum mean discrepancy (MMD) measure-based hypothesis test. Next, we will show that our proposed test will outperform frequentist MMD-based methods by yielding a smaller false rejection and acceptance rate of the null. Finally, we will show that we can embed our proposed hypothesis testing procedure within a generative adversarial network (GAN) framework as an application of our method. Using our novel BNP hypothesis test, this new GAN approach can help to mitigate the lack of diversity in the generated samples and produce a more accurate inferential algorithm compared to traditional techniques.

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Section: Previous Workmentioning

confidence: 99%

A Semi-Bayesian Nonparametric Hypothesis Test Using Maximum Mean Discrepancy with Applications in Generative Adversarial Networks

Fazeli-Asl¹,

Zhang²,

Lin³

2023

Preprint

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“…We chose GSE48000, a public free database of VTE from GEO, to evaluate the value of these six hub genes. Subsequently the ROC (20) was established, and to quantify its value, the area under the curve (AUC) and 95% con dence interval (CI) were calculated.…”

Section: The Value Of Hub Genes Evaluationmentioning

confidence: 99%

The Identification of Hub Genes in Diagnosing Blunt Trauma with Venous Thromboembolism Using Integrated Bioinformatic Analysis

Sun

Tan

Wei

et al. 2023

Preprint

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Background Whether blunt trauma patient will get venous thromboembolism (VTE) is still a huge challenge to forecast. The objective of our study is to investigate and identify the potential biomarker genes for blunt trauma patients with VTE based on the method of bioinformatics analysis. Methods To investigate differentially expressed genes (DEGs) of blunt trauma and VTE, firstly we used the data obtained from the gene expression omnibus (GEO) database, and DEGs were respectively obtained via LIMMA. Next Weighted gene co-expression network analysis (WGCNA) was used to investigate the co-expression network of blunt trauma DEGs and two modules were found. We chose the most significant module as the key one in correlation with blunt trauma. We got intersection of DEGs in blunt trauma and VTE, which were analyzed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Then we used STRING to create a protein-protein interaction (PPI) network, and hub genes were screened by Cytoscape software and analyzed of functional enrichment. We chose another GEO database to evaluate the value of hub genes, and receiver operating characteristic (ROC) curves were done at last. Results There are a total of 546 DEGs of VTE and 4266 DEGs of blunt trauma were screened out from GSE19151 and GSE36809 respectively. Then 1568 DEGs of blunt trauma were identified in the most significant module. And 101 intersections of DEGs were found to be enriched for various functions and pathways. Among these DEGs, 6 overlapped hub genes with high degrees of stress method were selected. These hub genes include MRPL3, RPLP0, MRPL15, TP53, MYC, CD3D.And the six hub genes were tested to have high diagnostic values.

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Evaluating model fit for type II censored data: a Bayesian non-parametric approach based on the Kullback-Leibler divergence estimation

Al-Labadi,

Fazeli-Asl,

2024

Communications in Statistics - Simulation and Computation

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ROC Analyses Based on Measuring Evidence Using the Relative Belief Ratio

Cited by 3 publications

References 30 publications

A Semi-Bayesian Nonparametric Hypothesis Test Using Maximum Mean Discrepancy with Applications in Generative Adversarial Networks

A Semi-Bayesian Nonparametric Hypothesis Test Using Maximum Mean Discrepancy with Applications in Generative Adversarial Networks

The Identification of Hub Genes in Diagnosing Blunt Trauma with Venous Thromboembolism Using Integrated Bioinformatic Analysis

Evaluating model fit for type II censored data: a Bayesian non-parametric approach based on the Kullback-Leibler divergence estimation

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