A Fully Bayesian, Logistic Regression Tracking Algorithm for Mitigating Disparate Misclassification

传染病患病率估计中的不完美检验：频率学派与贝叶斯逻辑回归方法及误分类校正的比较

Prevalence estimation in infectious diseases with imperfect tests: A comparison of Frequentist and Bayesian Logistic Regression methods with misclassification correction

本研究比较四种校正误分类的统计方法估计STI患病率，发现贝叶斯内部校正模型在低患病率下估计更稳定、CI更窄，优于Liu模型和外部校正法。

Tailored Bayes: a risk modelling framework under unequal misclassification costs

Risk prediction models are a crucial tool in healthcare. Risk prediction models with a binary outcome (i.e., binary classification models) are often constructed using methodology which assumes the costs of different classification errors are equal. In many healthcare applications this assumption is not valid, and the differences between misclassification costs can be quite large. For instance, in a diagnostic setting, the cost of misdiagnosing a person with a life-threatening disease as healthy may be larger than the cost of misdiagnosing a healthy person as a patient. In this work, we present…

关于稀疏广义贝叶斯逻辑回归的高维分类

On high-dimensional classification by sparse generalized Bayesian logistic regression

本文针对高维分类问题，提出稀疏广义贝叶斯逻辑回归方法，利用分数似然后验，推导了分数后验的集中结果及误分类超额风险界，并扩展到模型误设情形。

电子健康记录中疫苗接种状态的错误分类：集群随机试验中的贝叶斯方法

Misclassification of Vaccination Status in Electronic Health Records: A Bayesian Approach in Cluster Randomized Trials

针对电子健康记录中疫苗接种状态错误分类问题，提出一种贝叶斯逻辑回归扩展方法，通过专家先验扰动诊所数据，用于集群随机试验的敏感性分析，并在退伍军人疫苗接种研究中验证。

利用信息先验的贝叶斯更新提前预测学生表现

Early Prediction of Student Performance Using Bayesian Updating with Informative Priors Across Cohorts

本文通过利用前一届学生数据的贝叶斯更新方法，提升跨班级学生表现预测的鲁棒性，结果显示在早期周数内，信息先验能显著降低误判率和假阴性率。

A Descriptive Study of Variable Discretization and Cost-Sensitive Logistic Regression on Imbalanced Credit Data

Training classification models on imbalanced data tends to result in bias towards the majority class. In this paper, we demonstrate how variable discretization and cost-sensitive logistic regression help mitigate this bias on an imbalanced credit scoring dataset, and further show the application of the variable discretization technique on the data from other domains, demonstrating its potential as a generic technique for classifying imbalanced data beyond credit socring. The performance measurements include ROC curves, Area under ROC Curve (AUC), Type I Error, Type II Error, accuracy, and F1 s…