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作者：Lavancier, Frederic; Le Guevel, Ronan
作者单位：Nantes Universite; Universite de Rennes; Centre National de la Recherche Scientifique (CNRS)
摘要：Many spatiotemporal data record the time of birth and death of individuals, along with their spatial trajectories during their lifetime, whether through continuous-time observations or discrete-time observations. Natural applications include epidemiology, individual-based modelling in ecology, spatiotemporal dynamics observed in bioimaging and computer vision. The aim of this article is to estimate in this context the birth and death intensity functions that depend in full generality on the cu...
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作者：Heller; Rosset
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作者：Lei, Lihua; Candes, Emmanuel J.
作者单位：Stanford University; Stanford University
摘要：Evaluating treatment effect heterogeneity widely informs treatment decision making. At the moment, much emphasis is placed on the estimation of the conditional average treatment effect via flexible machine learning algorithms. While these methods enjoy some theoretical appeal in terms of consistency and convergence rates, they generally perform poorly in terms of uncertainty quantification. This is troubling since assessing risk is crucial for reliable decision-making in sensitive and uncertai...
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作者：Liu, Yi; Rockova, Veronika; Wang, Yuexi
作者单位：University of Chicago; University of Chicago
摘要：Few problems in statistics are as perplexing as variable selection in the presence of very many redundant covariates. The variable selection problem is most familiar in parametric environments such as the linear model or additive variants thereof. In this work, we abandon the linear model framework, which can be quite detrimental when the covariates impact the outcome in a non-linear way, and turn to tree-based methods for variable selection. Such variable screening is traditionally done by pr...
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作者：Kim, Byol; Liu, Song; Kolar, Mladen
作者单位：University of Chicago; University of Bristol; Alan Turing Institute; University of Chicago
摘要：Markov networks are frequently used in sciences to represent conditional independence relationships underlying observed variables arising from a complex system. It is often of interest to understand how an underlying network differs between two conditions. In this paper, we develop methods for comparing a pair of high-dimensional Markov networks where we allow the number of observed variables to increase with the sample sizes. By taking the density ratio approach, we are able to learn the netw...
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作者：Crane, Harry; Xu, Min
作者单位：Rutgers University System; Rutgers University New Brunswick
摘要：The spread of infectious disease in a human community or the proliferation of fake news on social media can be modelled as a randomly growing tree-shaped graph. The history of the random growth process is often unobserved but contains important information such as the source of the infection. We consider the problem of statistical inference on aspects of the latent history using only a single snapshot of the final tree. Our approach is to apply random labels to the observed unlabelled tree and...
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作者：Heng, Siyu; Kang, Hyunseung; Small, Dylan S.; Fogarty, Colin B.
作者单位：University of Pennsylvania; University of Wisconsin System; University of Wisconsin Madison; Massachusetts Institute of Technology (MIT)
摘要：In many observational studies, the interest is in the effect of treatment on bad, aberrant outcomes rather than the average outcome. For such settings, the traditional approach is to define a dichotomous outcome indicating aberration from a continuous score and use the Mantel-Haenszel test with matched data. For example, studies of determinants of poor child growth use the World Health Organization's definition of child stunting being height-for-age z-score <= - 2. The traditional approach may...
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作者：Meng, Kun; Eloyan, Ani
作者单位：Brown University
摘要：We propose a framework of principal manifolds to model high-dimensional data. This framework is based on Sobolev spaces and designed to model data of any intrinsic dimension. It includes principal component analysis and principal curve algorithm as special cases. We propose a novel method for model complexity selection to avoid overfitting, eliminate the effects of outliers and improve the computation speed. Additionally, we propose a method for identifying the interiors of circle-like curves ...
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作者：Johansson, Per; Rubin, Donald B.; Schultzberg, Marten
作者单位：Uppsala University; Tsinghua University
摘要：Blocking is commonly used in randomized experiments to increase efficiency of estimation. A generalization of blocking removes allocations with imbalance in covariate distributions between treated and control units, and then randomizes within the remaining set of allocations with balance. This idea of rerandomization was formalized by Morgan and Rubin (Annals of Statistics, 2012, 40, 1263-1282), who suggested using Mahalanobis distance between treated and control covariate means as the criteri...
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作者：Yu, Mengjia; Chen, Xiaohui
作者单位：University of Illinois System; University of Illinois Urbana-Champaign
摘要：Cumulative sum (CUSUM) statistics are widely used in the change point inference and identification. For the problem of testing for existence of a change point in an independent sample generated from the mean-shift model, we introduce a Gaussian multiplier bootstrap to calibrate critical values of the CUSUM test statistics in high dimensions. The proposed bootstrap CUSUM test is fully data dependent and it has strong theoretical guarantees under arbitrary dependence structures and mild moment c...
