Recent Advances in Anytime-Valid Inference
报告人：谢楚焓（304am永利集团）
时间：2024-10-31 16:00-17:00
地点：腾讯会议 568-7810-5726
Abstract:
Traditional statistical inference for parameters is based on asymptotic coverage guarantees at fixed sample sizes. Such guarantees tend to become problematic in sequential experimental design due to the issue of "p-hacking". In recent years, there has been an emerging literature on safe anytime-valid inference that focuses on mitigating this problem and provide valid coverage guarantees for any stopping times. The main technique is called the supermartingale method, coupled with Ville's inequality.
In this talk, we give a detailed discuss on the original intuition, popular applications, and recent advances on anytime-valid inference. Particularly, we will focus on its usage in hypothesis testing of parametric examples, construction of confidence sequences for sample means, and modification to adapt to asymptotic theory. We will finally introduce an application to parameter inference in stochastic approximation.
