3期

最新文章

• Jiangfeng Wang, Rong Jiang, Keming Yu's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Wang, Jiangfeng; Yu, Keming; Jiang, Rong
  作者单位：Zhejiang Gongshang University; Brunel University; Shanghai University of International Business & Economics
  摘要：We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning. These popular doubly robust estimators combine outcome modelling with balancing weights-weights that achieve covariate balance directly instead of estimating and inverting the propensity score. When the outcome and weighting models are both linear in some (possibly infinite) basis, we show that the augmented estimator is equivalent to a single linear model with coefficients th...
• Jiangfeng Wang, Rong Jiang, Keming Yu's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Wang, Jiangfeng; Yu, Keming; Jiang, Rong
  作者单位：Zhejiang Gongshang University; Brunel University; Shanghai University of International Business & Economics
  摘要：We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning. These popular doubly robust estimators combine outcome modelling with balancing weights-weights that achieve covariate balance directly instead of estimating and inverting the propensity score. When the outcome and weighting models are both linear in some (possibly infinite) basis, we show that the augmented estimator is equivalent to a single linear model with coefficients th...
• Safaa K. Kadhem's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Kadhem, Safaa K.
  作者单位：Al-Muthanna University
• DecoR: deconfounding time series with robust regression AI翻译
  作者：Schur, Felix; Blieske, Pio; Peters, Jonas
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：Causal inference on time series data is a challenging problem, especially in the presence of unobserved confounders. In this work, we focus on estimating the causal effect of a multivariate time series on a univariate time series when a third (possibly multivariate) time series confounds the relationship but remains unobserved. By assuming spectral sparsity of the confounder, we show how this problem can be framed as an adversarial outlier problem in the frequency domain. We introduce Deconfou...
• Nicholas T. Longford's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Longford, Nicholas T.
  作者单位：Warsaw School of Economics
• Inference with Mondrian random forests AI翻译
  作者：Cattaneo, Matias Damian; Klusowski, Jason Matthew; Underwood, William George
  作者单位：Princeton University; University of Cambridge
  摘要：Random forests are popular methods for regression and classification analysis, and many different variants have been proposed in recent years. One interesting example is the Mondrian random forest, in which the underlying constituent trees are constructed via a Mondrian process. We give precise bias and variance characterizations, along with a Berry-Esseen-type central limit theorem, for the Mondrian random forest regression estimator. By combining these results with a carefully crafted debias...
• Generalized universal inference on risk minimizers AI翻译
  作者：Dey, Neil; Martin, Ryan; Williams, Jonathan P.
  作者单位：North Carolina State University
  摘要：A common goal in statistics and machine learning is estimation of unknowns. Point estimates alone are of little value without an accompanying measure of uncertainty, but traditional uncertainty quantification methods, such as confidence sets and p-values, often require distributional or structural assumptions that may not be justified in modern applications. The present paper considers a very common case in machine learning, where the quantity of interest is the minimizer of a given risk (expe...
• Pitman efficiency lower bounds for multivariate distribution-free tests based on optimal transport AI翻译
  作者：Deb, Nabarun; Bhattacharya, Bhaswar B.; Sen, Bodhisattva
  作者单位：University of Chicago; University of Pennsylvania; Columbia University
  摘要：The Wilcoxon rank sum test is one of the most popular distribution-free two-sample tests for univariate data. Among the important reasons for their popularity are the striking results of Hodges-Lehmann and Chernoff-Savage, where the authors show that the asymptotic (Pitman) relative efficiency of Wilcoxon's test compared to Student's t-test, never falls below 0.864 (with identity score) and 1 (with Gaussian score), respectively. Motivated by these results, we propose and study a large family o...
• Federated feature selection with false discovery rate control AI翻译
  作者：Hu, Jie; Tong, Jiayi; Ning, Yang; Tang, Cheng Yong; Moore, Jason H.; Li, Runze; Chen, Yong
  作者单位：University of Pennsylvania; Pennsylvania Medicine; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Cornell University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Cedars Sinai Medical Center; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：Selecting a set of universally relevant features associated with a given response variable across multiple distributed data sites is an important problem in numerous scientific fields. However, performing this federated feature selection task becomes challenging when individual-level data cannot be shared due to privacy concerns. The problem is further complicated by potential heterogeneity in both feature distributions and model parameters across sites. In this paper, we propose Fed-false dis...
• Zhu Shen and Jose R. Zubizarreta's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Shen, Zhu; Zubizarreta, Jose R.
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard Medical School; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University