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作者：Yang, Shu; Ding, Peng
作者单位：North Carolina State University; University of California System; University of California Berkeley
摘要：The era of big data has witnessed an increasing availability of multiple data sources for statistical analyses. We consider estimation of causal effects combining big main data with unmeasured confounders and smaller validation data withon these confounders. Under the unconfoundedness assumption with completely observed confounders, the smaller validation data allow for constructing consistent estimators for causal effects, but the big main data can only give error-prone estimators in general....
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作者：Castro-Camilo, Daniela; Huser, Raphael
作者单位：King Abdullah University of Science & Technology; University of Glasgow
摘要：To disentangle the complex nonstationary dependence structure of precipitation extremes over the entire contiguous United States (U.S.), we propose a flexible local approach based on factor copula models. Our subasymptotic spatial modeling framework yields nontrivial tail dependence structures, with a weakening dependence strength as events become more extreme; a feature commonly observed with precipitation data but not accounted for in classical asymptotic extreme-value models. To estimate th...
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作者：Simoneau, Gabrielle; Moodie, Erica E. M.; Nijjar, Jagtar S.; Platt, Robert W.
作者单位：McGill University; University of Cambridge
摘要：The statistical study of precision medicine is concerned with dynamic treatment regimes (DTRs) in which treatment decisions are tailored to patient-level information. Individuals are followed through multiple stages of clinical intervention, and the goal is to perform inferences on the sequence of personalized treatment decision rules to be applied in practice. Of interest is the identification of an optimal DTR, that is, the sequence of treatment decisions that yields the best expected outcom...
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作者：Zheng, Chaowen; Wu, Yichao
作者单位：North Carolina State University; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
摘要：A multivariate mixture model is determined by three elements: the number of components, the mixing proportions, and the component distributions. Assuming that the number of components is given and that each mixture component has independent marginal distributions, we propose a nonparametric method to estimate the component distributions. The basic idea is to convert the estimation of component density functions to a problem of estimating the coordinates of the component density functions with ...
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作者：Breto, Carles; Ionides, Edward L.; King, Aaron A.
作者单位：University of Michigan System; University of Michigan; University of Valencia; University of Michigan System; University of Michigan
摘要：Panel data, also known as longitudinal data, consist of a collection of time series. Each time series, which could itself be multivariate, comprises a sequence of measurements taken on a distinct unit. Mechanistic modeling involves writing down scientifically motivated equations describing the collection of dynamic systems giving rise to the observations on each unit. A defining characteristic of panel systems is that the dynamic interaction between units should be negligible. Panel models the...
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作者：Li, Chunlin; Shen, Xiaotong; Pan, Wei
作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of Minnesota System; University of Minnesota Twin Cities
摘要：Inference of directional pairwise relations between interacting units in a directed acyclic graph (DAG), such as a regulatory gene network, is common in practice, imposing challenges because of lack of inferential tools. For example, inferring a specific gene pathway of a regulatory gene network is biologically important. Yet, frequentist inference of directionality of connections remains largely unexplored for regulatory models. In this article, we propose constrained likelihood ratio tests f...
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作者：Wilson, Douglas R.; Jin, Chong; Ibrahim, Joseph G.; Sun, Wei
作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; Fred Hutchinson Cancer Center; University of Washington; University of Washington Seattle
摘要：Immunotherapies have attracted lots of research interests recently. The need to understand the underlying mechanisms of immunotherapies and to develop precision immunotherapy regimens has spurred great interest in characterizing immune cell composition within the tumor microenvironment. Several methods have been developed to estimate immune cell composition using gene expression data from bulk tumor samples. However, these methods are not flexible enough to handle aberrant patterns of gene exp...
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作者：Bharath, Karthik; Kurtek, Sebastian
作者单位：University of Nottingham; University System of Ohio; Ohio State University
摘要：Alignment of curve data is an integral part of their statistical analysis, and can be achieved using model- or optimization-based approaches. The parameter space is usually the set of monotone, continuous warp maps of a domain. Infinite-dimensional nature of the parameter space encourages sampling based approaches, which require a distribution on the set of warp maps. Moreover, the distribution should also enable sampling in the presence of important landmark information on the curves which co...
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作者：Ricciardi, Federico; Mattei, Alessandra; Mealli, Fabrizia
作者单位：University of London; University College London; University of Florence
摘要：We focus on causal inference for longitudinal treatments, where units are assigned to treatments at multiple time points, aiming to assess the effect of different treatment sequences on an outcome observed at a final point. A common assumption in similar studies is sequential ignorability (SI): treatment assignment at each time point is assumed independent of future potential outcomes given past observed outcomes and covariates. SI is questionable when treatment participation depends on indivi...
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作者：Shen, Jieli; Liu, Regina Y.; Xie, Min-ge
作者单位：Rutgers University System; Rutgers University New Brunswick
摘要：Inferences from different data sources can often be fused together, a process referred to as fusion learning, to yield more powerful findings than those from individual data sources alone. Effective fusion learning approaches are in growing demand as increasing number of data sources have become easily available in this big data era. This article proposes a new fusion learning approach, called iFusion, for drawing efficient individualized inference by fusing learnings from relevant data source...
