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作者：Sergazinov, R.; Taeb, A.; Gaynanova, I
作者单位：Texas A&M University System; Texas A&M University College Station; University of Washington; University of Washington Seattle; University of Michigan System; University of Michigan
摘要：Multi-view data provide complementary information on the same set of observations, with multi-omics and multimodal sensor data being common examples. Analysing such data typically requires distinguishing between shared (joint) and unique (individual) signal subspaces from noisy, high-dimensional measurements. Despite many proposed methods, the conditions for reliably identifying joint and individual subspaces remain unclear. We rigorously quantify these conditions, which depend on the ratio of...
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作者：Viviano, Davide; Bradic, Jelena
作者单位：Harvard University; Cornell University
摘要：This article concerns the estimation and inference of treatment effects in panel data settings when treatments change dynamically over time. We propose a balancing method that allows for (i) treatments to be assigned dynamically over time based on high-dimensional covariates, past outcomes and treatments; (ii) outcomes and time-varying covariates to depend on the trajectory of all past treatments; and (iii) heterogeneity of treatment effects. Our approach recursively projects potential outcome...
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作者：Mauri, L.; Dunson, D. B.
作者单位：Duke University
摘要：This article focuses on inference in logistic regression for high-dimensional binary outcomes. A popular approach induces dependence across the outcomes by including latent factors in the linear predictor. Bayesian approaches are useful for characterizing uncertainty in inferring the regression coefficients, factors and loadings, while also incorporating hierarchical and shrinkage structures. However, Markov chain Monte Carlo algorithms for posterior computation face challenges in scaling to h...
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作者：Bolin, David; Wallin, Jonas
作者单位：King Abdullah University of Science & Technology; Lund University
摘要：The estimation of regression parameters in spatially referenced data plays a crucial role across various scientific domains. A common approach involves employing an additive regression model to capture the relationship between observations and covariates, accounting for spatial variability not explained by the covariates through a Gaussian random field. We study the effect of misspecified covariates, in particular when the misspecification changes the smoothness. We analyse the theoretical pro...
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作者：Grazzi, S.; Zanella, G.
作者单位：Bocconi University; Bocconi University
摘要：We develop parallel algorithms for simulating zeroth-order, also known as gradient-free, Metropolis Markov chains based on the Picard map. For random-walk Metropolis Markov chains targeting log-concave distributions $ \pi $ on $ \mathbb{R}<^>{d} $, our algorithm generates samples close to $ \pi $ in $ \mathcal{O}(\surd{d}) $ parallel iterations using $ \mathcal{O}(\surd{d}) $ processors, thereby speeding up the convergence of the corresponding sequential implementation by a factor $ \surd{d} $...
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作者：Kitagawa, Toru; Lee, Sokbae; Qiu, Chen
作者单位：Brown University; Columbia University; Cornell University
摘要：Following Savage (1951)and Manski (2004), the literature on statistical treatment choice focuses on the mean of welfare regret. Ignoring other features of the regret distribution, however, can lead to a rule that is sensitive to sampling uncertainty. We propose to minimize the mean of a nonlinear transformation of regret and show that singleton rules are not essentially complete for nonlinear regret. Focusing on mean-square regret, we derive closed-form fractions for finite-sample Bayes and mi...
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作者：Sterzinger, P.; Kosmidis, I
作者单位：University of London; London School Economics & Political Science; University of Warwick
摘要：We characterize the behaviour of the maximum Diaconis-Ylvisaker prior penalized likelihood estimator in high-dimensional logistic regression, where the number of covariates is a fraction $ \kappa\in(0,1) $ of the number of observations $ n $, as $ n o\infty $. We construct a rescaled estimator with zero asymptotic aggregate bias, and define adjusted $ Z $-statistics and rescaled penalized likelihood ratio statistics that exhibit the typical null asymptotic distributions, when the covariates ar...
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作者：Zu, Tianhai; Qin, Yichen
作者单位：University of Texas System; University of Texas at San Antonio; University System of Ohio; University of Cincinnati
摘要：In network analysis, one frequently needs to conduct inference for network parameters based on a single observed network. Since the sampling distribution of the statistic is often unknown, one has to rely on the bootstrap. However, because of the complex dependence structure among vertices, existing bootstrap methods often yield unsatisfactory performance, especially for small or moderate sample sizes. Here we propose a new network bootstrap procedure, termed the local bootstrap, for estimatin...
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作者：Gang, B.; Banerjee, T.
作者单位：Fudan University; University of Kansas
摘要：Heteroskedasticity poses several methodological challenges in designing valid and powerful procedures for simultaneous testing of composite null hypotheses. In particular, the conventional practice of standardizing or rescaling heteroskedastic test statistics in this setting may severely affect the power of the underlying multiple testing procedure. Additionally, when the inferential parameter of interest is correlated with the variance of the test statistic, methods that ignore this dependenc...
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作者：Gui, Lin; Jiang, Yuchao; Wang, Jingshu
作者单位：University of Chicago; Texas A&M University System; Texas A&M University College Station
摘要：Combining dependent $ p $-values poses a long-standing challenge in statistical inference, particularly when aggregating findings from multiple methods to enhance signal detection. Recently, $ p $-value combination tests based on regularly-varying-tailed distributions, such as the Cauchy combination test and harmonic mean $ p $-value, have attracted attention for their robustness to unknown dependence. This paper provides a theoretical and empirical evaluation of these methods under an asympto...
