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BFI Working Paper·Mar 31, 2023
Prediction When Factors are Weak
Stefano Giglio, Dacheng Xiu, and Dake Zhang
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Uncategorized
BFI Working Paper·Feb 13, 2023
Covariate Adjustment in Experiments with Matched Pairs
Yuehao Bai, Liang Jiang, Joseph P. Romano, Azeem Shaikh, and Yichong Zhang
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Uncategorized
BFI Working Paper·Nov 12, 2021
Finite- and Large-Sample Inference for Ranks using Multinomial Data with an Application to Ranking Political Parties
Sergei Bazylik, Magne Mogstad, Joseph P. Romano, Azeem Shaikh, and Daniel Wilhelm
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BFI Working Paper·Jul 7, 2021
Test Assets and Weak Factors
Stefano Giglio, Dacheng Xiu, and Dake Zhang
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Financial Markets
BFI Working Paper·Oct 16, 2020
How Well Generative Adversarial Networks Learn Distributions
Tengyuan Liang
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Technology & Innovation
BFI Working Paper·Oct 16, 2020
Estimating Certain Integral Probability Metrics (IPMs) Is as Hard as Estimating under the IPMs
Tengyuan Liang
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Technology & Innovation
BFI Working Paper·Oct 16, 2020
A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum-L1-Norm Interpolated Classifiers
Tengyuan Liang and Pragya Sur
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Technology & Innovation
BFI Working Paper·Oct 16, 2020
Mehler’s Formula, Branching Process, and Compositional Kernels of Deep Neural Networks
Tengyuan Liang and Hai Tran-Bach
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Technology & Innovation
BFI Working Paper·Oct 5, 2020
An Adversarial Approach to Structural Estimation
Tetsuya Kaji, Elena Manresa, and Guillaume Pouliot
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Economic Mobility & Poverty
BFI Working Paper·Sep 18, 2020
Inference for Large-Scale Linear Systems with Known Coefficients
Zheng Fang, Andres Santos, Azeem Shaikh, and Alexander Torgovitsky
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