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作者：Chen, Li; Li, Chunlin; Shen, Xiaotong; Pan, Wei
作者单位：University of Minnesota System; University of Minnesota Twin Cities; Iowa State University; University of Minnesota System; University of Minnesota Twin Cities
摘要：This article proposes a novel causal discovery and inference method called GrIVET for a Gaussian directed acyclic graph with unmeasured confounders. GrIVET consists of an order-based causal discovery method and a likelihood-based inferential procedure. For causal discovery, we generalize the existing peeling algorithm to estimate the ancestral relations and candidate instruments in the presence of hidden confounders. Based on this, we propose a new procedure for instrumental variable estimatio...
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作者：Song, Yan; Khalid, Zubair; Genton, Marc G.
作者单位：King Abdullah University of Science & Technology; Lahore University of Management Sciences
摘要：Earth system models (ESMs) are fundamental for understanding Earth's complex climate system. However, the computational demands and storage requirements of ESM simulations limit their utility. For the newly published CESM2-LENS2 data, which suffer from this issue, we propose a novel stochastic generator (SG) as a practical complement to the CESM2, capable of rapidly producing emulations closely mirroring training simulations. Our SG leverages the spherical harmonic transformation (SHT) to shif...
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作者：Chen, Sida; Finkenstaedt, Baerbel
作者单位：University of Warwick; MRC Biostatistics Unit; University of Cambridge
摘要：B-spline-based hidden Markov models employ B-splines to specify the emission distributions, offering a more flexible modeling approach to data than conventional parametric HMMs. We introduce a Bayesian framework for inference, enabling the simultaneous estimation of all unknown model parameters including the number of states. A parsimonious knot configuration of the B-splines is identified by the use of a trans-dimensional Markov chain sampling algorithm, while model selection regarding the nu...
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作者：Gao, Jiti; Peng, Bin; Yan, Yayi
作者单位：Monash University; Shanghai University of Finance & Economics
摘要：In this article, we propose a simple inferential method for a wide class of panel data models with a focus on such cases that have both serial correlation and cross-sectional dependence. In order to establish an asymptotic theory to support the inferential method, we develop some new and useful higher-order expansions, such as Berry-Esseen bound and Edgeworth Expansion, under a set of simple and general conditions. We further demonstrate the usefulness of these theoretical results by explicitl...
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作者：Cavaliere, Giuseppe; Goncalves, Silvia; Nielsen, Morten Orregaard; Zanelli, Edoardo
作者单位：University of Bologna; McGill University; Aarhus University
摘要：We consider bootstrap inference for estimators which are (asymptotically) biased. We show that, even when the bias term cannot be consistently estimated, valid inference can be obtained by proper implementations of the bootstrap. Specifically, we show that the prepivoting approach of Beran, originally proposed to deliver higher-order refinements, restores bootstrap validity by transforming the original bootstrap p-value into an asymptotically uniform random variable. We propose two different i...
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作者：Park, Chan; Chen, Guanhua; Yu, Menggang; Kang, Hyunseung
作者单位：University of Pennsylvania; University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison
摘要：When developing policies for prevention of infectious diseases, policymakers often set specific, outcome-oriented targets to achieve. For example, when developing a vaccine allocation policy, policymakers may want to distribute them so that at least a certain fraction of individuals in a census block are disease-free and spillover effects due to interference within blocks are accounted for. The article proposes methods to estimate a block-level treatment policy that achieves a predefined, outc...
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作者：Cabral, Rafael; Bolin, David; Rue, Havard
作者单位：King Abdullah University of Science & Technology
摘要：Latent Gaussian models (LGMs) are perhaps the most commonly used class of models in statistical applications. Nevertheless, in areas ranging from longitudinal studies in biostatistics to geostatistics, it is easy to find datasets that contain inherently non-Gaussian features, such as sudden jumps or spikes, that adversely affect the inferences and predictions made using an LGM. These datasets require more general latent non-Gaussian models (LnGMs) that can handle automatically these non-Gaussi...
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作者：Wang, Bingkai; Park, Chan; Small, Dylan S.; Li, Fan
作者单位：University of Pennsylvania; Yale University; Yale University
摘要：Cluster-randomized experiments are increasingly used to evaluate interventions in routine practice conditions, and researchers often adopt model-based methods with covariate adjustment in the statistical analyses. However, the validity of model-based covariate adjustment remains unclear when the working models are misspecified, leading to ambiguity of estimands and risk of bias. In this article, we first adapt two model-based methods-generalized estimating equations and linear mixed models-wit...
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作者：Zhou, Le; Cook, R. Dennis; Zou, Hui
作者单位：Hong Kong Baptist University; University of Minnesota System; University of Minnesota Twin Cities
摘要：Huber regression (HR) is a popular flexible alternative to the least squares regression when the error follows a heavy-tailed distribution. We propose a new method called the enveloped Huber regression (EHR) by considering the envelope assumption that there exists some subspace of the predictors that has no association with the response, which is referred to as the immaterial part. More efficient estimation is achieved via the removal of the immaterial part. Different from the envelope least s...
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作者：Prentice, Ross L.
作者单位：University of Washington; University of Washington Seattle; Fred Hutchinson Cancer Center
