Recently Published
Nonparametric Reliability Estimation with Simultaneous Confidence Bands: Bootstrap versus Borell-TIS
Reliability analysis studies how long a system or component continues to perform its intended function before
failure. When lifetimes are subject to right-censoring, meaning some units have not failed by the end of
observation, the Kaplan–Meier (KM) estimator provides a nonparametric estimate of the reliability (or
survival) function: R(t) = P(T > t), where T denotes lifetime.
Setup Instructions
This document provides instructions on how to download and install the software and tools needed for HGEN 611: Data Science I
"Actuarial Claim Cost Modeling: Comparing Linear Regression, Tweedie GLM, and XGBoost Ensembles"
A practical comparison of four approaches for modeling insurance claim costs – Linear Regression, Two‑Part (Logistic+Gamma), Tweedie GLM, and XGBoost with Tweedie loss. Using simulated car insurance data, this project demonstrates the actuarial edge of the Tweedie distribution for zero‑inflated, skewed claim data. All models perform similarly, with XGBoost achieving a marginal 2.21% RMSE improvement. The real value lies in feature importance – revealing past claims and mileage as the strongest predictors.
Obesity
Obesity is a growing global health concern strongly linked to behavioral and lifestyle
factors such as diet, physical activity, alcohol consumption, and transportation habits.
This project proposes a categorical data analysis approach to uncover direct
statistical associations and visualization of how lifestyle behaviors correspond with
obesity categories, providing an interpretable and health-relevant perspective.
Beyond the Historical: Spatial Voting Typologies in Poland
Spatial Machine Learning Analysis of the 2023 Parliamentary Election
Zuzanna Herniczek, Weidong Bai
A Modern Semi-Structural Monetary Policy Model for Brazil
A Monthly MPP Adaptation Inspired by Bogdanski, Tombini & Werlang (2000)
Inspired by the BCB Small-Scale Model (MPP) — Bogdanski, Tombini & Werlang (2000)
June 18, 2026