IDSS Winter Celebration
All IDSS and extended IDSS community members welcome, including students, postdocs, faculty, and staff. Snacks provided!
Winners with Confidence: Discrete Argmin Inference with an Application to Model Selection
Abstract:Â We study the problem of finding the index of the minimum value of a vector from noisy observations. This problem is relevant in population/policy comparison, discrete maximum likelihood, and model selection. By integrating concepts and tools from cross-validation and differential privacy, we develop a test statistic that is asymptotically normal even in high-dimensional settings, and allows for arbitrarily many ties in the population mean vector. The key technical ingredient is a central limit theorem for globally dependent data characterized…