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作者：Qiao, Xinghao; Qian, Cheng; James, Gareth M.; Guo, Shaojun
作者单位：University of London; London School Economics & Political Science; University of Southern California; Renmin University of China
摘要：We consider estimating a functional graphical model from multivariate functional observations. In functional data analysis, the classical assumption is that each function has been measured over a densely sampled grid. However, in practice the functions have often been observed, with measurement error, at a relatively small number of points. We propose a class of doubly functional graphical models to capture the evolving conditional dependence relationship among a large number of sparsely or de...
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作者：Wang, Y. Samuel; Drton, Mathias
作者单位：University of Chicago; Technical University of Munich
摘要：We consider graphical models based on a recursive system of linear structural equations. This implies that there is an ordering, sigma, of the variables such that each observed variable Y-v is a linear function of a variable-specific error term and the other observed variables Y-u with sigma(u) < sigma(v). The causal relationships, i.e., which other variables the linear functions depend on, can be described using a directed graph. It has previously been shown that when the variable-specific er...
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作者：Rosenbaum, P. R.
作者单位：University of Pennsylvania
摘要：In an observational study matched for observed covariates, an association between treatment received and outcome exhibited may indicate not an effect caused by the treatment, but merely some bias in the allocation of treatments to individuals within matched pairs. The evidence that distinguishes moderate biases from causal effects is unevenly dispersed among possible comparisons in an observational study: some comparisons are insensitive to larger biases than others. Intuitively, larger treatm...
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作者：Yang, S.; Pieper, K.; Cools, F.
作者单位：North Carolina State University; Duke University
摘要：Structural failure time models are causal models for estimating the effect of time-varying treatments on a survival outcome. G-estimation and artificial censoring have been proposed for estimating the model parameters in the presence of time-dependent confounding and administrative censoring. However, most existing methods require manually pre-processing data into regularly spaced data, which may invalidate the subsequent causal analysis. Moreover, the computation and inference are challenging...
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作者：Huang, Jing; Ning, Yang; Reid, Nancy; Chen, Yong
作者单位：University of Pennsylvania; Cornell University; University of Toronto
摘要：Composite likelihood functions are often used for inference in applications where the data have a complex structure. While inference based on the composite likelihood can be more robust than inference based on the full likelihood, the inference is not valid if the associated conditional or marginal models are misspecified. In this paper, we propose a general class of specification tests for composite likelihood inference. The test statistics are motivated by the fact that the second Bartlett i...
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作者：Meng, Cheng; Zhang, Xinlian; Zhang, Jingyi; Zhong, Wenxuan; Ma, Ping
作者单位：University System of Georgia; University of Georgia
摘要：We consider the problem of approximating smoothing spline estimators in a nonparametric regression model. When applied to a sample of size n, the smoothing spline estimator can be expressed as a linear combination of n basis functions, requiring O(n(3)) computational time when the number d of predictors is two or more. Such a sizeable computational cost hinders the broad applicability of smoothing splines. In practice, the full-sample smoothing spline estimator can be approximated by an estima...
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作者：Gao, Chao; Ma, Zongming
作者单位：University of Chicago; University of Pennsylvania
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作者：Vihola, Matti; Franks, Jordan
作者单位：University of Jyvaskyla
摘要：Approximate Bayesian computation enables inference for complicated probabilistic models with intractable likelihoods using model simulations. The Markov chain Monte Carlo implementation of approximate Bayesian computation is often sensitive to the tolerance parameter: low tolerance leads to poor mixing and large tolerance entails excess bias. We propose an approach that involves using a relatively large tolerance for the Markov chain Monte Carlo sampler to ensure sufficient mixing and post-pro...
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作者：Wang, Xuan; Parast, Layla; Tian, Lu; Cai, Tianxi
作者单位：Zhejiang University; RAND Corporation; Stanford University; Harvard University
摘要：In randomized clinical trials, the primary outcome, Y, often requires long-term follow-up and/or is costly to measure. For such settings, it is desirable to use a surrogate marker, S, to infer the treatment effect on Y, Delta. Identifying such an S and quantifying the proportion of treatment effect on Y explained by the effect on S are thus of great importance. Most existing methods for quantifying the proportion of treatment effect are model based and may yield biased estimates under model mi...
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作者：Mao, Lu
作者单位：University of Wisconsin System; University of Wisconsin Madison
摘要：The infinite-dimensional information operator for the nuisance parameter plays a key role in semiparametric inference, as it is closely related to the regular estimability of the target parameter. Calculation of information operators has traditionally proceeded in a case-by-case manner and has often entailed lengthy derivations with complicated arguments. We develop a unified framework for this task by exploiting commonality in the form of semiparametric likelihoods. The general formula develo...
