-
作者：Rybak, J.; Battey, H. S.; Bharath, K.
作者单位：Imperial College London; University of Nottingham
摘要：That parameterization and sparsity are inherently linked raises the possibility that relevant models, not obviously sparse in their natural formulation, exhibit a population-level sparsity after reparameterization. In covariance models, positive definiteness enforces additional constraints on how sparsity can legitimately manifest. It is therefore natural to consider reparameterization maps in which sparsity respects positive definiteness. This paper provides insight into structures on the phy...
-
作者：Wan, P.
作者单位：Erasmus University Rotterdam; Erasmus University Rotterdam - Excl Erasmus MC
摘要：In this paper, we characterize the extremal dependence of $ d $ asymptotically dependent variables using a class of random vectors on the $ (d-1) $-dimensional hyperplane perpendicular to the diagonal vector $ \mathbf{1}=(1,\ldots,1) $. This translates analyses of multivariate extremes to analyses on a linear vector space, opening up possibilities for applying existing statistical techniques based on linear operations. As an example, we demonstrate how to obtain lower-dimensional approximation...
-
作者：Cho, Yanghyeon; Berg, Emily
作者单位：Columbia University; Iowa State University
摘要：Estimating the mean square error of a small area predictor under an informative sampling design is a challenging problem. Existing approaches rely on approximations that have not been justified theoretically. We provide rigorous support for a mean square error estimator that is applicable to an informative sample design. The procedure can be used in combination with predictors of general parameters that may be nonlinear functions of the model response variable. We also construct calibrated pre...
-
作者：Tam, Edric; Dunson, David B.; Duan, Leo L.
作者单位：Stanford University; Duke University; State University System of Florida; University of Florida
摘要：Tree graphs are used routinely in statistics. When estimating a Bayesian model with a tree component, sampling the posterior remains a core difficulty. Existing Markov chain Monte Carlo methods tend to rely on local moves, often leading to poor mixing. A promising approach is to instead directly sample spanning trees on an auxiliary graph. Current spanning tree samplers, such as the celebrated Aldous-Broder algorithm, rely predominantly on simulating random walks that are required to visit all...
-
作者：Xu, Tong; Taeb, Armeen; Kucukyavuz, Simge; Shojaie, Ali
作者单位：Northwestern University; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
摘要：We study the problem of learning directed acyclic graphs from continuous observational data, generated according to a linear Gaussian structural equation model. State-of-the-art structure learning methods for this setting have at least one of the following shortcomings: (i) they cannot provide optimality guarantees and can suffer from learning suboptimal models; (ii) they rely on the stringent assumption that the noise is homoscedastic, and hence the underlying model is fully identifiable. We ...
-
作者：Aronow, P. M.; Chang, Haoge; Lopatto, Patrick
作者单位：Yale University; Columbia University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
摘要：We consider the problem of generating confidence sets in randomized experiments with noncompliance. We show that a refinement of a randomization-based procedure proposed by Imbens & Rosenbaum (2005) has desirable properties. Specifically, we show that using a studentized Anderson-Rubin statistic as a test statistic yields confidence sets that are finite-sample exact under treatment effect homogeneity and remain asymptotically valid for the local average treatment effect when the treatment effe...
-
作者：Fong, E.; Yiu, A.
作者单位：University of Hong Kong; University of Southampton
摘要：The martingale posterior framework replaces the elicitation of the likelihood and prior with that of a sequence of one-step-ahead predictive densities for Bayesian inference. Posterior sampling then involves the imputation of unobserved quantities and can then be carried out in an expedient and parallelizable manner using predictive resampling, without requiring Markov chain Monte Carlo. Recent work has investigated the use of plug-in parametric predictive densities, combined with stochastic g...
-
作者：Kent, A.; Berrett, T. B.; Yu, Y.
作者单位：University of Warwick
摘要：We consider the problem of two-sample testing under a local differential privacy constraint using a permutation procedure. We develop testing procedures that are optimal up to logarithmic factors for general discrete distributions and continuous distributions subject to a smoothness constraint. Both noninteractive and interactive tests are considered, and we show that allowing interactivity results in an improvement in the minimax separation rates. Our results show that permutation procedures ...
-
作者：Ghosh, Aditya; Deb, Nabarun; Karmakar, Bikram; Sen, Bodhisattva
作者单位：Stanford University; University of Wisconsin System; University of Wisconsin Madison; Columbia University
摘要：Mean-based estimators of causal effects in randomized experiments may behave poorly if the potential outcomes have a heavy tail or contain outliers. An alternative estimator proposed by estimates a constant additive treatment effect by inverting a randomization test using ranks. We develop a design-based asymptotic theory for this rank-based estimator and study its robustness and efficiency properties. We show that Rosenbaum's estimator is robust against outliers with a breakdown point that un...
-
作者：Koo, Taehyeon; Pashley, Nicole E.
作者单位：Columbia University; Rutgers University System; Rutgers University New Brunswick
摘要：Researchers often turn to block randomization to increase the precision of their inference or for practical reasons, such as in multi-site trials. However, if the number of treatments under consideration is large, it may not be feasible or practical to assign all treatments within each block. We develop novel inference results under the finite-population, design-based framework for natural alternatives to the complete block design that do not require reducing the number of treatment arms, name...
