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作者：Dasgupta, Tirthankar; Pillai, Natesh S.; Rubin, Donald B.
作者单位：Harvard University
摘要：A framework for causal inference from two-level factorial designs is proposed, which uses potential outcomes to define causal effects. The paper explores the effect of non-additivity of unit level treatment effects on Neyman's repeated sampling approach for estimation of causal effects and on Fisher's randomization tests on sharp null hypotheses in these designs. The framework allows for statistical inference from a finite population, permits definition and estimation of estimands other than a...
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作者：Khare, Kshitij; Oh, Sang-Yun; Rajaratnam, Bala
作者单位：State University System of Florida; University of Florida; Stanford University
摘要：Sparse high dimensional graphical model selection is a topic of much interest in modern day statistics. A popular approach is to apply l(1)-penalties to either parametric likelihoods, or regularized regression/pseudolikelihoods, with the latter having the distinct advantage that they do not explicitly assume Gaussianity. As none of the popular methods proposed for solving pseudolikelihood-based objective functions have provable convergence guarantees, it is not clear whether corresponding esti...
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作者：Lavancier, Frederic; Moller, Jesper; Rubak, Ege
作者单位：Nantes Universite; Aalborg University
摘要：Statistical models and methods for determinantal point processes (DPPs) seem largely unexplored. We demonstrate that DPPs provide useful models for the description of spatial point pattern data sets where nearby points repel each other. Such data are usually modelled by Gibbs point processes, where the likelihood and moment expressions are intractable and simulations are time consuming. We exploit the appealing probabilistic properties of DPPs to develop parametric models, where the likelihood...
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作者：Yin, Xiangrong; Hilafu, Haileab
作者单位：University of Kentucky; University of Tennessee System; University of Tennessee Knoxville
摘要：We propose a new and simple framework for dimension reduction in the large p, small n setting. The framework decomposes the data into pieces, thereby enabling existing approaches for n>p to be adapted to n
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作者：Cao, Hongyuan; Zeng, Donglin; Fine, Jason P.
作者单位：University of Chicago; University of North Carolina; University of North Carolina Chapel Hill
摘要：We consider estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent responses and covariates are observed intermittently within subjects. Unlike with synchronous data, where the response and covariates are observed at the same time point, with asynchronous data, the observation times are mismatched. Simple kernel-weighted estimating equations are proposed for generalized linear models with either time invariant or time-dependent coefficients unde...
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作者：Kraus, David
作者单位：University of Lausanne; Centre Hospitalier Universitaire Vaudois (CHUV)
摘要：Functional data are traditionally assumed to be observed on the same domain. Motivated by a data set of heart rate temporal profiles, we develop methodology for the analysis of incomplete functional samples where each curve may be observed on a subset of the domain and unobserved elsewhere. We formalize this observation regime and develop the fundamental procedures of functional data analysis for this framework: estimation of parameters (mean and covariance operator) and principal component an...
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作者：Hao, Ning; Dong, Bin; Fan, Jianqing
作者单位：University of Arizona; Princeton University
摘要：Many high dimensional classification techniques have been proposed in the literature based on sparse linear discriminant analysis. To use them efficiently, sparsity of linear classifiers is a prerequisite. However, this might not be readily available in many applications, and rotations of data are required to create the sparsity needed. We propose a family of rotations to create the sparsity required. The basic idea is to use the principal components of the sample covariance matrix of the pool...
