A1514
Title: Unified framework for inference using confidence sets for the CDF
Authors: Siddhaarth Sarkar - Carnegie Mellon University (United States) [presenting]
Arun Kuchibhotla - Carnegie Mellon University (United States)
Abstract: Traditional statistical inference methods often face limitations due to their reliance on strict assumptions. Moreover, these methods are typically tailored to specific assumptions, restricting their adaptability to any alternative set of assumptions. A unified framework is presented for deriving confidence intervals for various functionals (e.g., mean or median) under a broad class of user-specified assumptions (e.g., finite variance or tail behavior). Leveraging confidence sets for cumulative distribution functions (CDFs), this framework offers a principled and flexible inference strategy, reducing dependence on stringent assumptions and providing applicability in diverse contexts.
