stat.ME · 2026-08-27 · No. 97
Methodology, 2026-08-27.
3 new papers in stat.ME. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
3 entries-
01
Controlling for Omitted Variable Bias in Deep Neural Networks
Manuel Pfeuffer, Roshan Prakash Rane, Kerstin Ritter, Sonja Greven
stat.ME · cs.CV · cs.LG
Control variables are widely used in statistical modelling to account for omitted variable bias of known confounders. However, they have largely been underexplored in deep learning. This is surprising, given that deep learning models encode image-inferable covariates, such as demographic variables, into their predictions when these covariates are correlated with the outcome---a form of omitted variable bias referred to as 'shortcut learning'....
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02
Functional linear regression from sparse to dense designs: a pooling-ridge method and minimax optimality
Shunxing Yan, Fang Yao
stat.ME · cs.LG · math.ST · stat.ML
Functional data analysis is an important statistical field that treats data as random functions. In practice, the random functions are often not fully observed but instead measured at discrete times. While simpler problems, such as mean and covariance estimation, have been widely studied for discretely observed data, optimal estimation of linear regression for this data type has remained unsolved for over two decades. To tackle this...
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03
SAUSS: Stochastic Approximation with Unbiased Simulated Scores for Limited Dependent Variable Models
Sokbae Lee, Yuan Liao, Myung Hwan Seo, Youngki Shin
stat.ME · cs.LG · econ.EM
Multinomial choice models allow flexible substitution patterns but become computationally demanding with many alternatives or observations. With a fixed per-observation simulation budget, simulated maximum likelihood introduces simulation bias, while each optimization step requires a full-sample likelihood evaluation. We propose Stochastic Approximation with Unbiased Simulated Scores (SAUSS), an averaged stochastic approximation based on...
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