-
作者：Cai, Tianxi; Li, Mengyan; Liu, Molei
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard Medical School; Bentley University; Columbia University
摘要：In this work, we propose a Semi-supervised Triply Robust Inductive transFer LEarning (STRIFLE) approach, which integrates heterogeneous data from a label-rich source population and a label-scarce target population and uses a large amount of unlabeled data simultaneously to improve the learning accuracy in the target population. Specifically, we consider a high dimensional covariate shift setting and employ two nuisance models, a density ratio model and an imputation model, to combine transfer ...
-
作者：Kundig, Pascal; Sigrist, Fabio
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Basel
摘要：Latent Gaussian process (GP) models are flexible probabilistic nonparametric function models. Vecchia approximations are accurate approximations for GPs to overcome computational bottlenecks for large data, and the Laplace approximation is a fast method with asymptotic convergence guarantees to approximate marginal likelihoods and posterior predictive distributions for non-Gaussian likelihoods. Unfortunately, the computational complexity of combined Vecchia-Laplace approximations grows faster ...
-
作者：Zuo, Shuozhi; Ghosh, Debashis; Ding, Peng; Yang, Fan
作者单位：Colorado School of Public Health; University of California System; University of California Berkeley; Tsinghua University; Yanqi Lake Beijing Institute of Mathematical Sciences & Applications
摘要：Mediation analysis is widely used for investigating direct and indirect causal pathways through which an effect arises. However, many mediation analysis studies are challenged by missingness in the mediator and outcome. In general, when the mediator and outcome are missing not at random, the direct and indirect effects are not identifiable without further assumptions. We study the identifiability of the direct and indirect effects under some interpretable mechanisms that allow for missing not ...
-
作者：Bu, Qiushi; Liang, Hua; Zhang, Xinyu; Zou, Jiahui
作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; George Washington University; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Capital University of Economics & Business
摘要：Tensors have broad applications in neuroimaging, data mining, digital marketing, etc. CANDECOMP/PARAFAC (CP) tensor decomposition can effectively reduce the number of parameters to gain dimensionality-reduction and thus plays a key role in tensor regression. However, in CP decomposition, there is uncertainty about which rank to use. In this article, we develop a model averaging method to handle this uncertainty by weighting the estimators from candidate tensor regression models with different ...
-
作者：Zhang, Yuhua; Dempsey, Walter
作者单位：University of Michigan System; University of Michigan; Harvard University
摘要：Scientists are increasingly interested in discovering community structure from modern relational data arising on large-scale social networks. While many methods have been proposed for learning community structure, few account for the fact that these modern networks arise from processes of interactions in the population. We introduce block edge exchangeable models (BEEM) for the study of interaction networks with latent node-level community structure. The block vertex components model (B-VCM) i...
-
作者：Shin, Sooahn; Lim, Johan; Park, Jong Hee
作者单位：Harvard University; Seoul National University (SNU); Seoul National University (SNU)
摘要：Ideal point estimation methods in the social sciences lack a principled approach for identifying multidimensional ideal points. We present a novel method for estimating multidimensional ideal points based on l(1) distance. In the Bayesian framework, the use of l(1) distance transforms the invariance problem of infinite rotational turns into the signed perpendicular problem, yielding posterior estimates that contract around a small area. Our simulation shows that the proposed method successfull...
-
作者：Gradu, Paula; Zrnic, Tijana; Wang, Yixin; Jordan, Michael I.
作者单位：University of California System; University of California Berkeley; University of Michigan System; University of Michigan; University of California System; University of California Berkeley
摘要：Causal discovery and causal effect estimation are two fundamental tasks in causal inference. While many methods have been developed for each task individually, statistical challenges arise when applying these methods jointly: estimating causal effects after running causal discovery algorithms on the same data leads to double dipping, invalidating the coverage guarantees of classical confidence intervals. To this end, we develop tools for valid post-causal-discovery inference. Across empirical ...
-
作者：Elmasri, Mohamad
作者单位：University of Toronto; Alan Turing Institute
摘要：Bayesian inference for undirected graphical models is mostly restricted to the class of decomposable graphs, as they enjoy a rich set of properties making them amenable to high-dimensional problems. While parameter inference is straightforward in this setup, inferring the underlying graph is a challenge driven by the computational difficulty in exploring the space of decomposable graphs. This work makes two contributions to address this problem. First, we provide sufficient and necessary condi...
-
作者：Zhao, Zinan; Sun, Wenguang
作者单位：Zhejiang University; Zhejiang University; Zhejiang University
摘要：The effective utilization of structural information in data while ensuring statistical validity poses a significant challenge in false discovery rate (FDR) analyses. Conformal inference provides rigorous theory for grounding complex machine learning methods without relying on strong assumptions or highly idealized models. However, existing conformal methods have limitations in handling structured multiple testing, as their validity often requires the deployment of symmetric decision rules, whi...
