Muandet, K; Jitkrittum, W; Kübler, JM; (2020) Kernel conditional moment test via maximum moment restriction. In: Peters, J and Sontag, D, (eds.) Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI). (pp. pp. 41-50). Proceedings of Machine Learning Research

Preview

Text muandet20a-supp.pdf - Published Version Download (575kB) | Preview

Abstract

We propose a new family of specification tests called kernel conditional moment (KCM) tests. Our tests are built on a novel representation of conditional moment restrictions in a reproducing kernel Hilbert space (RKHS) called conditional moment embedding (CMME). After transforming the conditional moment restrictions into a continuum of unconditional counterparts, the test statistic is defined as the maximum moment restriction (MMR) within the unit ball of the RKHS. We show that the MMR not only fully characterizes the original conditional moment restrictions, leading to consistency in both hypothesis testing and parameter estimation, but also has an analytic expression that is easy to compute as well as closed-form asymptotic distributions. Our empirical studies show that the KCM test has a promising finite-sample performance compared to existing tests.

Download activity - last month

Export as XMLJSONCSV

Download activity - last 12 months

Export as XMLJSONCSV

Downloads by country - last 12 months

Export as JSONXMLCSV

Archive Staff Only

View Item