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作者：Zhang, Yao; Zhao, Qingyuan
作者单位：National University of Singapore; University of Cambridge
摘要：Sensitivity analysis for the unconfoundedness assumption is crucial in observational studies. For this purpose, the marginal sensitivity model has gained popularity in recent years owing to its good interpretability and mathematical properties. However, most existing models only consider a worst-case parameter that bounds the logit difference between the observed-data and full-data propensity scores, which may not fully capture the extent of unmeasured confounding. We propose a new sensitivity...
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作者：Gilbert, Brian; Ogburn, Elizabeth L.; Datta, Abhirup
作者单位：Johns Hopkins University
摘要：This article addresses the asymptotic performance of popular spatial regression estimators of the linear effect of an exposure on an outcome under spatial confounding, the presence of an unmeasured spatially structured variable influencing both the exposure and the outcome. We first show that the estimators from ordinary least squares and restricted spatial regression are asymptotically biased under spatial confounding. We then prove a novel result on the infill consistency of the generalized ...
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作者：Baddeley, A.; Davies, T. M.; Hazelton, M. L.
作者单位：Curtin University; University of Otago
摘要：The pair correlation function, or two-point correlation, of a spatial point process is a fundamental tool in spatial statistics and astrostatistics, measuring the strength of spatial dependence between points. Interest is focused on the behaviour of this function at short distances, but this is the region in which existing estimators can be particularly unreliable. We propose a new estimator of the pair correlation function based on techniques from stochastic geometry and kernel density estima...
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作者：Cen, Zetai; Lam, Clifford
作者单位：University of Bristol; University of London; London School Economics & Political Science
摘要：We propose a test for the Kronecker product structure of a factor loading matrix implied by a tensor factor model with Tucker decomposition in the common component. By defining a Kronecker product structure set, we determine whether a tensor time series has a Kronecker product structure, equivalent to its ability to decompose the series according to a tensor factor model. Our test is built on analysing and comparing the residuals from fitting a full tensor factor model, and the residuals from ...
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作者：Heng, J.; De Bortoli, V; Doucet, A.; Thornton, J.
作者单位：ESSEC Business School; Universite PSL; Ecole Normale Superieure (ENS); University of Oxford
摘要：We consider the problem of simulating diffusion bridges, which are diffusion processes that are conditioned to initialize and terminate at two given states. The simulation of diffusion bridges has applications in diverse scientific fields and plays a crucial role in the statistical inference of discretely observed diffusions. This is known to be a challenging problem that has received much attention in the last two decades. This article contributes to this rich body of literature by presenting...
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作者：Xu, Jiazhen; Wood, Andrew T. A.; Zou, Tao
作者单位：Australian National University
摘要：Functional data analysis offers a diverse toolkit of statistical methods tailored to analysing samples of real-valued random functions. Recently, samples of time-varying random objects, such as time-varying networks, have been increasingly encountered in data analysis. These data structures represent elements within general metric spaces that lack local or global linear structures, rendering traditional functional data analysis methods inapplicable. Moreover, the existing methodology for time-...
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作者：Zhang, Chao; Geng, Zhi; Li, Wei; Ding, Peng
作者单位：Beijing Technology & Business University; Renmin University of China; Renmin University of China; University of California System; University of California Berkeley
摘要：Although the existing causal inference literature focuses on the forward-looking perspective by estimating effects of causes, the backward-looking perspective can provide insights into causes of effects. In backward-looking causal inference, the probability of necessity measures the probability that a certain event is caused by the treatment, given the observed treatment and outcome. Most existing results focus on binary outcomes. Motivated by applications with ordinal outcomes, we propose a g...
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作者：Clausen, David S.; Teichman, Sarah, V; Willis, Amy D.
作者单位：University of Washington; University of Washington Seattle
摘要：We consider the problem of estimating ratios of means of a multivariate outcome across covariates when the data are observed with unknown sample-specific and category-specific perturbations. Our model admits a partially identifiable estimand, and we establish full identifiability by imposing interpretable parameter constraints. To reduce bias and guarantee the existence of estimators in the presence of sparse observations, we apply an asymptotically negligible and constraint-invariant penalty ...
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作者：Dunson, David B.; Wu, Nan
作者单位：Duke University; University of Texas System; University of Texas Dallas
摘要：It is often of interest to infer lower-dimensional structure underlying complex data. As a flexible class of nonlinear structures, it is common to focus on Riemannian manifolds. Most existing manifold-learning algorithms replace the original data with lower-dimensional coordinates without providing an estimate of the manifold or using it to denoise the original data. This article proposes a new methodology to address these issues, allowing interpolation of the estimated manifold between the fi...
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作者：Graham, E.; Carone, M.; Rotnitzky, A.
