时间：2024年7月11日（周四）16:00-17:00
地点：西湖大学云谷校区E4-233
主持人：理论科学研究院 周珍楠
主讲人：新墨西哥大学 张国鹢
讲座主题：Small Area Estimation using Support Vector Machine
讲座摘要: The Fay-Herriot model (Fay&Herriot,1979) and the Battese, Harter, and Fuller (BHF) model (Battese, Carter, &Fuller,1988) are commonly used in small area estimation (SAE), assuming a linear relationship between response and predictor variables, which may not always hold. This study extends both models to more flexible semi-parametric versions (SP-SAEArea and SP-SAEUnit) where nonparametric components are estimated using machine learning tools like support vector machines. We propose two backfitting algorithms to address the SP-SAEArea and SP-SAEUnit problems. Using American Community Survey (ACS) data and simulations, our SP-SAEArea and SP-SAEUnit models significantly enhance estimation accuracy by simultaneously reducing bias and variance when linear relationships are not met. This research underscores the importance of integrating machine learning techniques into small area estimation to improve accuracy by addressing model assumptions.
