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作者：Ekvall, Karl Oskar; Bottai, Matteo
作者单位：University of Florida; State University System of Florida; University of Florida; Karolinska Institutet
摘要：We provide finite-sample distribution approximations that are uniform in the parameter for inference in linear mixed models. The focus is on variances and covariances of random effects in cases where existing theory fails because the covariance matrix is nearly or exactly singular and hence near or at the boundary of the parameter set. Quantitative bounds on the differences between the standard normal density and densities of linear combinations of the score function enable, for example, the a...
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作者：Garcia-Portugues, E.; Sorensen, M.
作者单位：University of Copenhagen
摘要：We provide a class of diffusion processes for continuous time-varying multivariate angular data with explicit transition probability densities, enabling exact likelihood inference. The presented diffusions are time reversible and can be constructed for any prespecified stationary distribution on the torus, including highly multimodal mixtures. We give results on asymptotic likelihood theory, allowing one-sample inference and tests of linear hypotheses for $ k $ groups of diffusions, including ...
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作者：Martin, A.; Santacatterina, M.; Diaz, I
作者单位：New York University
摘要：Marginal structural models are a popular method for estimating causal effects in the presence of time-varying exposures. In spite of their popularity, no scalable nonparametric estimator exists for marginal structural models with multi-valued or continuous time-varying treatments. In this paper, we combine flexible, data-adaptive regression methods, including ensemble learning techniques, with recent developments in semiparametric efficiency theory for longitudinal studies to propose such an e...
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作者：Park, Beomjo; Balakrishnan, Sivaraman; Wasserman, Larry
作者单位：Carnegie Mellon University
摘要：In statistical inference, it is rarely realistic to assume that the hypothesized statistical model is well specified; consequently, it is important to understand the effects of misspecification on inferential procedures. When the hypothesized statistical model is misspecified, the natural target of inference is a projection of the data-generating distribution onto the model. We present a general method for constructing valid confidence sets for such projections, under weak regularity condition...
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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：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...
