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作者：Zhou, Hang; Yao, Fang; Zhang, Huiming
作者单位：Peking University; University of Macau
摘要：Despite extensive studies on functional linear regression, there exists a fundamental gap in theory between the ideal estimation from fully observed covariate functions and the reality that one can only observe functional covariates discretely with noise. The challenge arises when deriving a sharp perturbation bound for the estimated eigenfunctions in the latter case, which renders existing techniques for functional linear regression not applicable. We use a pooling method to attain the estima...
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作者：Tudball, Matthew J.; Hughes, Rachael A.; Tilling, Kate; Bowden, Jack; Zhao, Qingyuan
作者单位：University of Bristol; University of Exeter; University of Cambridge
摘要：Many partial identification problems can be characterized by the optimal value of a function over a set where both the function and set need to be estimated by empirical data. Despite some progress for convex problems, statistical inference in this general setting remains to be developed. To address this, we derive an asymptotically valid confidence interval for the optimal value through an appropriate relaxation of the estimated set. We then apply this general result to the problem of selecti...
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作者：Wang, Shulei
作者单位：University of Illinois System; University of Illinois Urbana-Champaign
摘要：Differential abundance tests for compositional data are essential and fundamental in various biomedical applications, such as single-cell, bulk RNA-seq and microbiome data analysis. However, because of the compositional constraint and the prevalence of zero counts in the data, differential abundance analysis on compositional data remains a complicated and unsolved statistical problem. This article proposes a new differential abundance test, the robust differential abundance test, to address th...
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作者：Guo, Kevin; Rothenhausler, Dominik
作者单位：Stanford University
摘要：In observational causal inference, exact covariate matching plays two statistical roles: (i) it effectively controls for bias due to measured confounding; (ii) it justifies assumption-free inference based on randomization tests. In this paper we show that inexact covariate matching does not always play these same roles. We find that inexact matching often leaves behind statistically meaningful bias, and that this bias renders standard randomization tests asymptotically invalid. We therefore re...
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作者：Kreiss, J. P.; Paparoditis, E.
作者单位：Braunschweig University of Technology; University of Cyprus
摘要：Fitting parametric models by optimizing frequency-domain objective functions is an attractive approach of parameter estimation in time series analysis. Whittle estimators are a prominent example in this context. Under weak conditions and the assumption that the true spectral density of the underlying process does not necessarily belong to the parametric class of spectral densities fitted, the distribution of Whittle estimators typically depends on difficult to estimate characteristics of the u...
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作者：Luo, Lan; Wang, Jingshen; Hector, Emily C.
作者单位：Rutgers University System; University of California System; University of California Berkeley; North Carolina State University
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作者：Schultheiss, C.; Buhlmann, P.
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：We present a new method for causal discovery in linear structural equation models. We propose a simple technique based on statistical testing in linear models that can distinguish between ancestors and non-ancestors of any given variable. Naturally, this approach can then be extended to estimating the causal order among all variables. Unlike with many methods, it is possible to provide explicit error control for false causal discovery, at least asymptotically. This holds even under Gaussianity...
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作者：Shi, J.; Wu, Z.; Dempsey, W.
作者单位：University of Michigan System; University of Michigan
摘要：The micro-randomized trial is a sequential randomized experimental design to empirically evaluate the effectiveness of mobile health intervention components that may be delivered at hundreds or thousands of decision points. Micro-randomized trials have motivated a new class of causal estimands, termed causal excursion effects, for which semiparametric inference can be conducted via a weighted, centred least-squares criterion (Boruvka et al., 2018). Causal excursion effects allow health scienti...
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作者：Klaassen, S.; Kueck, J.; Spindler, M.; Chernozhukov, V
作者单位：University of Hamburg; Massachusetts Institute of Technology (MIT)
摘要：Graphical models have become a popular tool for representing dependencies within large sets of variables and are crucial for representing causal structures. We provide results for uniform inference on high-dimensional graphical models, in which the number of target parameters d is potentially much larger than the sample size, under approximate sparsity. Our results highlight how graphical models can be estimated and recovered using modern machine learning methods in high-dimensional complex se...
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作者：Cui, Y.; Michael, H.; Tanser, F.; Tchetgen, E. Tchetgen
作者单位：National University of Singapore; University of Massachusetts System; University of Massachusetts Amherst; University of Lincoln; University of Pennsylvania
摘要：Robins (1998) introduced marginal structural models, a general class of counterfactual models for the joint effects of time-varying treatments in complex longitudinal studies subject to time-varying confounding. Robins (1998) established the identification of marginal structural model parameters under a sequential randomization assumption, which rules out unmeasured confounding of treatment assignment over time. The marginal structural Cox model is one of the most popular marginal structural m...
