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作者：Lee, Myoung-jae
作者单位：Korea University
摘要：Given an endogenous/confounded binary treatment D, a response Y with its potential versions (Y-0, Y-1) and covariates X, finding the treatment effect is difficult if Y is not continuous, even when a binary instrumental variable (IV) Z is available. We show that, for any form of Y (continuous, binary, mixed, horizontal ellipsis ), there exists a decomposition Y = mu(0)(X) + mu(1)(X)D + error with E(error|Z,X) = 0, where mu 1(X)equivalent to E(Y1-Y0|complier,X) and 'compliers' are those who get ...
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作者：Ferraccioli, Federico; Arnone, Eleonora; Finos, Livio; Ramsay, James O.; Sangalli, Laura M.
作者单位：University of Padua; Polytechnic University of Milan; University of Padua; McGill University
摘要：We propose a nonparametric method for density estimation over (possibly complicated) spatial domains. The method combines a likelihood approach with a regularization based on a differential operator. We demonstrate the good inferential properties of the method. Moreover, we develop an estimation procedure based on advanced numerical techniques, and in particular making use of finite elements. This ensures high computational efficiency and enables great flexibility. The proposed method efficien...
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作者：Tan, Linda S. L.
作者单位：National University of Singapore
摘要：We propose using model reparametrization to improve variational Bayes inference for hierarchical models whose variables can be classified as global (shared across observations) or local (observation-specific). Posterior dependence between local and global variables is minimized by applying an invertible affine transformation on the local variables. The functional form of this transformation is deduced by approximating the posterior distribution of each local variable conditional on the global ...
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作者：Chen, Mingli; Kato, Kengo; Leng, Chenlei
作者单位：University of Warwick; Cornell University; University of Warwick
摘要：Data in the form of networks are increasingly available in a variety of areas, yet statistical models allowing for parameter estimates with desirable statistical properties for sparse networks remain scarce. To address this, we propose the Sparse beta-Model (S beta M), a new network model that interpolates the celebrated Erdos-Renyi model and the beta-model that assigns one different parameter to each node. By a novel reparameterization of the beta-model to distinguish global and local paramet...
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作者：Chen, Xu; Tokdar, Surya T.
作者单位：Duke University
摘要：Linear quantile regression is a powerful tool to investigate how predictors may affect a response heterogeneously across different quantile levels. Unfortunately, existing approaches find it extremely difficult to adjust for any dependency between observation units, largely because such methods are not based upon a fully generative model of the data. For analysing spatially indexed data, we address this difficulty by generalizing the joint quantile regression model of Yang and Tokdar (Journal ...
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作者：Wijayatunga, Priyantha
作者单位：Umea University
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作者：Wang, Xiangyu; Leng, Chenlei; Boot, Tom
作者单位：Alphabet Inc.; Google Incorporated; University of Warwick; University of Groningen
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作者：Chai, Christine P.
作者单位：Microsoft
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作者：Windmeijer, Frank; Liang, Xiaoran; Hartwig, Fernando P.; Bowden, Jack
作者单位：University of Oxford; University of Oxford; University of Bristol; University of Bristol; University of Exeter
摘要：We propose a new method, the confidence interval (CI) method, to select valid instruments from a larger set of potential instruments for instrumental variable (IV) estimation of the causal effect of an exposure on an outcome. Invalid instruments are such that they fail the exclusion conditions and enter the model as explanatory variables. The CI method is based on the CIs of the per instrument causal effects estimates and selects the largest group with all CIs overlapping with each other as th...
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作者：Mateu, Jorge
作者单位：Universitat Jaume I
