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pro vyhledávání: '"Ham, Daeyoung"'
We propose new methods for multivariate linear regression when the regression coefficient matrix is sparse and the error covariance matrix is dense. We assume that the error covariance matrix has equicorrelation across the response variables. Two pro
Externí odkaz:
http://arxiv.org/abs/2410.10025
We consider the problem of causal inference based on observational data (or the related missing data problem) with a binary or discrete treatment variable. In that context we study counterfactual density estimation, which provides more nuanced inform
Externí odkaz:
http://arxiv.org/abs/2403.19917
Autor:
Ham, Daeyoung, Rothman, Adam J.
We propose a penalized least-squares method to fit the linear regression model with fitted values that are invariant to invertible linear transformations of the design matrix. This invariance is important, for example, when practitioners have categor
Externí odkaz:
http://arxiv.org/abs/2307.03317