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作者：Ogburn, Elizabeth L.; Rotnitzky, Andrea; Robins, James M.
作者单位：Johns Hopkins University; Universidad Torcuato Di Tella; Harvard University
摘要：We consider estimation of the causal effect of a binary treatment on an outcome, conditionally on covariates, from observational studies or natural experiments in which there is a binary instrument for treatment. We describe a doubly robust, locally efficient estimator of the parameters indexing a model for the local average treatment effect conditionally on covariates V when randomization of the instrument is only true conditionally on a high dimensional vector of covariates X, possibly bigge...
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作者：Zhou, Zhou
作者单位：University of Toronto
摘要：We consider statistical inference for time series linear regression where the response and predictor processes may experience general forms of abrupt and smooth non-stationary behaviours over time. Meanwhile, the regression parameters may be subject to linear inequality constraints. A simple and unified procedure for structural stability checks and parameter inference is proposed. In the case where the regression parameters are constrained, the methodology proposed is shown to be consistent wh...
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作者：Bolin, David; Lindgren, Finn
作者单位：Chalmers University of Technology; University of Bath
摘要：In several areas of application ranging from brain imaging to astrophysics and geostatistics, an important statistical problem is to find regions where the process studied exceeds a certain level. Estimating such regions so that the probability for exceeding the level in the entire set is equal to some predefined value is a difficult problem connected to the problem of multiple significance testing. In this work, a method for solving this problem, as well as the related problem of finding cred...
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作者：Lian, Heng; Liang, Hua; Carroll, Raymond J.
作者单位：Nanyang Technological University; George Washington University; Texas A&M University System; Texas A&M University College Station
摘要：We consider heteroscedastic regression models where the mean function is a partially linear single-index model and the variance function depends on a generalized partially linear single-index model. We do not insist that the variance function depends only on the mean function, as happens in the classical generalized partially linear single-index model. We develop efficient and practical estimation methods for the variance function and for the mean function. Asymptotic theory for the parametric...
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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：Gerber, Mathieu; Chopin, Nicolas
作者单位：University of Lausanne; Institut Polytechnique de Paris; ENSAE Paris; Institut Polytechnique de Paris; ENSAE Paris
摘要：We derive and study sequential quasi Monte Carlo (SQMC), a class of algorithms obtained by introducing QMC point sets in particle filtering. SQMC is related to, and may be seen as an extension of, the array-RQMC algorithm of L'Ecuyer and his colleagues. The complexity of SQMC is O{Nlog(N)}, where N is the number of simulations at each iteration, and its error rate is smaller than the Monte Carlo rate OP(N-1/2). The only requirement to implement SQMC algorithms is the ability to write the simul...
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作者：Cai, Juan-Juan; Einmahl, John H. J.; de Haan, Laurens; Zhou, Chen
作者单位：Delft University of Technology; Tilburg University; Erasmus University Rotterdam - Excl Erasmus MC; Erasmus University Rotterdam; Universidade de Lisboa; European Central Bank; De Nederlandsche Bank NV; Tinbergen Institute
摘要：Denote the loss return on the equity of a financial institution as X and that of the entire market as Y. For a given very small value of p>0, the marginal expected shortfall (MES) is defined as E{X|Y>QY(1-p)}, where Q(Y)(1-p) is the (1-p)th quantile of the distribution of Y. The MES is an important factor when measuring the systemic risk of financial institutions. For a wide non-parametric class of bivariate distributions, we construct an estimator of the MES and establish the asymptotic norma...
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作者：Hauser, Alain; Buehlmann, Peter
作者单位：University of Bern; Swiss Institute of Bioinformatics; Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：In many applications we have both observational and (randomized) interventional data. We propose a Gaussian likelihood framework for joint modelling of such different data types, based on global parameters consisting of a directed acyclic graph and corresponding edge weights and error variances. Thanks to the global nature of the parameters, maximum likelihood estimation is reasonable with only one or few data points per intervention. We prove consistency of the Bayesian information criterion ...
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作者：Zhang, Weiping; Leng, Chenlei; Tang, Cheng Yong
作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Warwick; National University of Singapore; University of Colorado System; University of Colorado Denver; Children's Hospital Colorado; University of Colorado Anschutz Medical Campus
摘要：In longitudinal studies, it is of fundamental importance to understand the dynamics in the mean function, variance function and correlations of the repeated or clustered measurements. For modelling the covariance structure, Cholesky-type decomposition-based approaches have been demonstrated to be effective. However, parsimonious approaches for directly revealing the correlation structure between longitudinal measurements remain less well explored, and existing joint modelling approaches may en...
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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...
