A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
FQE with deep neural networks achieves asymptotic normality and finite-sample bounds.
We consider two stage estimation with a non-parametric first stage and a generalized method of moments second stage, in a simpler setting than (Chernozhukov et al. 2016). We give an alternative proof of the theorem given in (Chernozhukov et al. 2016) that orthogonal second stage moments, sample splitting and -…
Statistical inference for misspecified contextual bandits is challenging due to adaptivity issues.
We introduce a novel regression framework which simultaneously models the quantile and the Expected Shortfall (ES) of a response variable given a set of covariates. This regression is based on a strictly consistent loss function for the pair quantile and ES, which allows for M- and Z-estimation of the joint regression …
New method improves statistical inference using machine learning-imputed data.
Improved concentration inequalities for sub-Weibull variables enhance statistical and machine learning applications.
Decomposes spillover effects under misspecified exposure mappings.
New method improves performance of Hamiltonian MCMC for log Z estimation.
Develops methods for estimating and providing confidence bands in sparse high-dimensional additive models.
We propose a new inferential framework for constructing confidence regions and testing hypotheses in statistical models specified by a system of high dimensional estimating equations. We construct an influence function by projecting the fitted estimating equations to a sparse direction obtained by solving a large-scale…
We consider non-parametric estimation and inference of conditional moment models in high dimensions. We show that even when the dimension of the conditioning variable is larger than the sample size , estimation and inference is feasible as long as the distribution of the conditioning variable has small intrinsic…
Predictive e-values enhance statistical inference across various tasks.
Unified ML imputation framework for missing data.