7 papers
On some practical challenges of conformal prediction
Liang Hong, Noura Raydan Nasreddine
Conformal prediction is a model-free machine learning method for constructing prediction regions at a guaranteed coverage probability level. However, a data scientist often faces t…
A new strategy for finite-sample valid prediction of future insurance claims in the regression setting
Liang Hong
The extant insurance literature demonstrates a paucity of finite-sample valid prediction intervals of future insurance claims in the regression setting. To address this challenge,…
The true detection probability versus the subjective detection probability of a uniformly optimal search plan
Liang Hong
This article investigates the difference between the true detection probability and the subjective probability of a uniformly optimal search plan. Its main contributions are multi-…
Conformal prediction of future insurance claims in the regression problem
Liang Hong
In the current insurance literature, prediction of insurance claims in the regression problem is often performed with a statistical model. This model-based approach may potentially…
On the true detection probability of the uniformly optimal search plan
Liang Hong
The gold standard for designing a search plan is to select a target distribution and then find the uniformly optimal search plan based on it. This approach has been successfully ap…
Causal Explainability of Machine Learning in Heart Failure Prediction from Electronic Health Records
Yina Hou, Shourav B. Rabbani, Liang Hong +2
The importance of clinical variables in the prognosis of the disease is explained using statistical correlation or machine learning (ML). However, the predictive importance of thes…