Reducing estimation risk using a Bayesian posterior distribution approach: Application to stress testing mortgage loan default

Wang, Zheqi; Crook, Jonathan; Andreeva, Galina

doi:10.1016/j.ejor.2020.04.020

Cited by 16 publications

(7 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Stress testing is becoming very important in the risk evaluation of banks and represents a key technique for risk management and capital decisions for financial institutions, as recognized by the Financial Services Authority (Financial Services Authority, 2008). Most of the approaches available in the literature for stress testing use dynamic models, such as survival models (Bellotti & Crook, 2013;Wang, Crook, & Andreeva, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Machine learning interpretability for a stress scenario generation in credit scoring based on counterfactuals

Bueff

Cytrynski

Calabrese

et al. 2022

Expert Systems with Applications

View full text Add to dashboard Cite

Section: Literature Reviewmentioning

confidence: 99%

Machine learning interpretability for a stress scenario generation in credit scoring based on counterfactuals

Bueff

Cytrynski

Calabrese

et al. 2022

Expert Systems with Applications

View full text Add to dashboard Cite

“…For example, a bag contains a number of black balls and white balls. People in the past thought that taking a ball, the probability of getting a white ball and a black ball was half, and they would not consider the impact of other factors on the results [17]. In academic circles, such a theory was called the frequency experiential school, which was a simple and limited theory until omas Bayes proposed the Bayesian theorem.…”

Section: Principle and Selection Of The Bayesian Algorithmmentioning

confidence: 99%

Design of the College Students’ Music Big Data Management System Based on Computer Assistance

Cheng

2022

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

With the rapid development of science and technology, the college music big data management system also needs a computer-aided data model for optimization. In order to improve the efficiency of computer-aided teaching of the music big data management system in colleges and universities, this paper analyzes and processes the music database of music software based on the computer-aided Bayesian algorithm and establishes a Bayesian model. Firstly, this paper introduces the principle and function theorem of the Bayesian algorithm. The data is divided into original music and new music, and a college music data management system with search recommendations as the core is established. We iteratively calculate the optimal hidden semantic matrix and we finally select ReLU function as the activation function of this experiment. After the model of the music management system is established, it is applied to a university. The experimental results show that the experiment has reached the expected standard. When the hidden factor dimension is 11, the system model has the best representation of music features. Students’ use of the music search and the recommendation system has greatly improved their music literacy and music coverage.

show abstract

“…The flexible hypotheses provide Bayesian methods with high predictive accuracy, making such methods as the preferred method for some areas [ 12 ]. Many reports on the application of Bayesian approach in various fields, such as hydrology [ 13 ], economics [ 14 ], clinic [ 15 ], management science [ 16 ], and electricity [ 17 ], have been published. However, the Bayesian method shows some limitations in practical applications.…”

Section: Introductionmentioning

confidence: 99%

A Method for Improving the Prediction of Outpatient Visits for Hospital Management: Bayesian Autoregressive Analysis

Wei

Tan

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

The number of outpatient visits is generally influenced by various factors that are difficult to quantify and obtain, resulting in some irregular fluctuations. The traditional statistical methodology seldom considers these uncertainties. Accordingly, this paper presents a Bayesian autoregressive (AR) analysis to propose a forecasting framework to cope with the strict requirements. The AR model was conducted to identify the linear and autocorrelation relationships of historical series, and Bayesian inference was used to correct and optimize the AR model parameters. Posterior distribution of parameters was stably and reliably obtained by Gibbs sampling on the condition of the convergent Markov chain. Meanwhile, the lag orders of the AR model were adjusted based on the series characteristics. To increase the variability and generality of the dataset, the developed Bayesian AR model was evaluated at seven hospitals in China. The results demonstrated that the Bayesian AR model had varying degrees of decline in the MAPE value in the seven sets of experimental data. The reductions ranged from 0.1431% to 0.0342%, indicating effective optimization of the Bayesian inference in the AR model parameters and reflecting the useful correction of the lag order adjustment strategy. The proposed Bayesian AR framework showed high accuracy index and stable prediction accuracy, thereby outperforming the traditional AR model.

show abstract

Reducing estimation risk using a Bayesian posterior distribution approach: Application to stress testing mortgage loan default

Cited by 16 publications

References 28 publications

Machine learning interpretability for a stress scenario generation in credit scoring based on counterfactuals

Machine learning interpretability for a stress scenario generation in credit scoring based on counterfactuals

Design of the College Students’ Music Big Data Management System Based on Computer Assistance

A Method for Improving the Prediction of Outpatient Visits for Hospital Management: Bayesian Autoregressive Analysis

Contact Info

Product

Resources

About