Zobrazeno 1 - 10
of 517
pro vyhledávání: '"Belloni, Alexandre"'
In this work we consider estimating the probability of many (possibly dependent) binary outcomes which is at the core of many applications, e.g., multi-level treatments in causal inference, demands for bundle of products, etc. Without further conditi
Externí odkaz:
http://arxiv.org/abs/2410.15166
We derive novel anti-concentration bounds for the difference between the maximal values of two Gaussian random vectors across various settings. Our bounds are dimension-free, scaling with the dimension of the Gaussian vectors only through the smaller
Externí odkaz:
http://arxiv.org/abs/2408.13348
Estimating causal effects has become an integral part of most applied fields. Solving these modern causal questions requires tackling violations of many classical causal assumptions. In this work we consider the violation of the classical no-interfer
Externí odkaz:
http://arxiv.org/abs/2212.03683
We propose a generalization of the linear panel quantile regression model to accommodate both \textit{sparse} and \textit{dense} parts: sparse means while the number of covariates available is large, potentially only a much smaller number of them hav
Externí odkaz:
http://arxiv.org/abs/1912.02151
Autor:
Alaei, Saeed1 (AUTHOR) saeed.a@gmail.com, Belloni, Alexandre2,3 (AUTHOR) alexandre.belloni@duke.edu, Makhdoumi, Ali3 (AUTHOR) ali.makhdoumi@duke.edu, Malekian, Azarakhsh4 (AUTHOR) azarakhsh.malekian@rotman.utoronto.ca
Publikováno v:
Operations Research. Nov/Dec2024, Vol. 72 Issue 6, p2413-2429. 17p.
We establish a central limit theorem for (a sequence of) multivariate martingales which dimension potentially grows with the length $n$ of the martingale. A consequence of the results are Gaussian couplings and a multiplier bootstrap for the maximum
Externí odkaz:
http://arxiv.org/abs/1809.02741
We consider a network of agents. Associated with each agent are her covariate and outcome. Agents influence each other's outcomes according to a certain connection/influence structure. A subset of the agents participate on a platform, and hence, are
Externí odkaz:
http://arxiv.org/abs/1808.04878
This paper considers inference for a function of a parameter vector in a partially identified model with many moment inequalities. This framework allows the number of moment conditions to grow with the sample size, possibly at exponential rates. Our
Externí odkaz:
http://arxiv.org/abs/1806.11466
This chapter presents key concepts and theoretical results for analyzing estimation and inference in high-dimensional models. High-dimensional models are characterized by having a number of unknown parameters that is not vanishingly small relative to
Externí odkaz:
http://arxiv.org/abs/1806.01888
Publikováno v:
In Journal of Econometrics May 2022 228(1):4-26