Robust estimation of outage costs in South Korea using a machine learning technique: Bayesian Tobit quantile regression

Risk factors based vessel‐specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method: Results from the PARADIGM registry

“…Quantile regression has recently been regarded as an efficient analysis tool for income and wage studies in labor economics. The Bayesian Tobit quantile regression, an advanced version of the plain quantile regression model, has been utilized to estimate outage costs in the engineering field 10–12 …”

“…The Bayesian Tobit quantile regression, an advanced version of the plain quantile regression model, has been utilized to estimate outage costs in the engineering field. [10][11][12] Although Wehby et al 20 first introduced the utility of the BQR model in the medical field by presenting the different risk factors for low and high birth weight, it is not widely adopted probably because its interpretation seems somewhat unintuitive since the concept of quantile is less familiar than means. 21 However, with the increased interest in machine learning methods in medical research, quantile regression has recently attracted attention as a valuable data analysis tool in the medical research area.…”

“…However, to date, most previous research has focused on obstructive CAD prediction via standard regression model analysis, overlooking the importance of the early stage of CAD as most deep and shallow machine learning models investigate only the average relationship between clinical outcome and risk factors. In contrast, the Bayesian quantile regression (BQR) model, a recently introduced machine learning method, is useful for analyzing the comprehensive association between clinical variables with various stages of CAD because BQR model yields multiple quantile regression curves 10–13 . Particularly useful for revealing hidden independent dynamic associations of target clinical variables according to quantile stages of endpoint in a complex database such as clinical data; thus, it can be applied to specific patients for tailored therapy.…”

“…In contrast, the Bayesian quantile regression (BQR) model, a recently introduced machine learning method, is useful for analyzing the comprehensive association between clinical variables with various stages of CAD because BQR model yields multiple quantile regression curves. [10][11][12][13] Particularly useful for revealing hidden independent dynamic associations of target clinical variables according to quantile stages of endpoint in a complex database such as clinical data; thus, it can be applied to specific patients for tailored therapy.…”

Reliability and cost analysis of the integrated emergency power system in building complex

“…The economics of sacrificing system operation to ensure the reliability of the system operation will be reconsidered. At present, many studies have proposed the customer damage function (CDF) (Kim et al, 2020), loss of load value (VOLL) (Swinand et al, 2019), expected unserved power (EUP) (Ma et al, 2021), EENS (Prajapati and Mahajan, 2021), and other indicators, combining reliability with economic for analysis. A bidding segment being marginal is computed via the probabilistic production simulation approach (Ma et al, 2021) while considering uncertainties of both demand and supply sides.…”