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作者：Ding, Peng; Lu, Jiannan
作者单位：University of California System; University of California Berkeley; Microsoft
摘要：Practitioners are interested in not only the average causal effect of a treatment on the outcome but also the underlying causal mechanism in the presence of an intermediate variable between the treatment and outcome. However, in many cases we cannot randomize the intermediate variable, resulting in sample selection problems even in randomized experiments. Therefore, we view randomized experiments with intermediate variables as semiobservational studies. In parallel with the analysis of observa...
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作者：Nandy, Siddhartha; Lim, Chae Young; Maiti, Tapabrata
作者单位：Michigan State University; Seoul National University (SNU)
摘要：Spatial regression is an important predictive tool in many scientific applications and an additive model provides a flexible regression relationship between predictors and a response variable. We develop a regularized variable selection technique for building a spatial additive model. We find that the methods developed for independent data do not work well for spatially dependent data. This motivates us to propose a spatially weighted l2-error norm with a group lasso type of penalty to select ...
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作者：Rao, Vinayak; Adams, Ryan P.; Dunson, David D.
作者单位：Purdue University System; Purdue University; Harvard University; Twitter, Inc.; Duke University
摘要：In many applications involving point pattern data, the Poisson process assumption is unrealistic, with the data exhibiting a more regular spread. Such repulsion between events is exhibited by trees for example, because of competition for light and nutrients. Other examples include the locations of biological cells and cities, and the times of neuronal spikes. Given the many applications of repulsive point processes, there is a surprisingly limited literature developing flexible, realistic and ...
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作者：Ji, Hao; Mueller, Hans-Georg
作者单位：University of California System; University of California Davis
摘要：We propose novel optimal designs for longitudinal data for the common situation where the resources for longitudinal data collection are limited, by determining the optimal locations in time where measurements should be taken. As for all optimal designs, some prior information is needed to implement the optimal designs proposed. We demonstrate that this prior information may come from a pilot longitudinal study that has irregularly measured and noisy measurements, where for each subject one ha...
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作者：Gao, Jiti; Han, Xiao; Pan, Guangming; Yang, Yanrong
作者单位：Monash University; Nanyang Technological University
摘要：Statistical inferences for sample correlation matrices are important in high dimensional data analysis. Motivated by this, the paper establishes a new central limit theorem for a linear spectral statistic of high dimensional sample correlation matrices for the case where the dimension p and the sample size n are comparable. This result is of independent interest in large dimensional random-matrix theory. We also further investigate the sample correlation matrices of a high dimensional vector w...
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作者：Gourieroux, Christian; Zakoian, Jean-Michel
作者单位：Institut Polytechnique de Paris; ENSAE Paris; University of Toronto; Universite de Lille
摘要：The non-causal auto-regressive process with heavy-tailed errors has non-linear causal dynamics, which allow for local explosion or asymmetric cycles that are often observed in economic and financial time series. It provides a new model for multiple local explosions in a strictly stationary framework. The causal predictive distribution displays surprising features, such as higher moments than for the marginal distribution, or the presence of a unit root in the Cauchy case. Aggregating such mode...
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作者：Brockwell, Peter J.; Matsuda, Yasumasa
作者单位：Colorado State University System; Colorado State University Fort Collins; Tohoku University
摘要：We define an isotropic Levy-driven continuous auto-regressive moving average CARMA(p,q) random field on Rn as the integral of a radial CARMA kernel with respect to a Levy sheet. Such fields constitute a parametric family characterized by an auto-regressive polynomial a and a moving average polynomial b having zeros in both the left and the right complex half-planes. They extend the well-balanced Ornstein-Uhlenbeck process of Schnurr and Woerner to a well-balanced CARMA process in one dimension...
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作者：Sun, Will Wei; Lu, Junwei; Liu, Han; Cheng, Guang
作者单位：Yahoo! Inc; Princeton University; Purdue University System; Purdue University
摘要：We propose a novel sparse tensor decomposition method, namely the tensor truncated power method, that incorporates variable selection in the estimation of decomposition components. The sparsity is achieved via an efficient truncation step embedded in the tensor power iteration. Our method applies to a broad family of high dimensional latent variable models, including high dimensional Gaussian mixtures and mixtures of sparse regressions. A thorough theoretical investigation is further conducted...
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作者：Oates, Chris J.; Girolami, Mark; Chopin, Nicolas
作者单位：University of Technology Sydney; University of Warwick; Alan Turing Institute; Institut Polytechnique de Paris; ENSAE Paris; Institut Polytechnique de Paris; ENSAE Paris
摘要：A non-parametric extension of control variates is presented. These leverage gradient information on the sampling density to achieve substantial variance reduction. It is not required that the sampling density be normalized. The novel contribution of this work is based on two important insights: a trade-off between random sampling and deterministic approximation and a new gradient-based function space derived from Stein's identity. Unlike classical control variates, our estimators improve rates...
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作者：Leeb, Hannes; Kabaila, Paul
作者单位：University of Vienna; La Trobe University
摘要：In the Gaussian linear regression model (with unknown mean and variance), we show that the standard confidence set for one or two regression coefficients is admissible in the sense of Joshi. This solves a long-standing open problem in mathematical statistics, and this has important implications on the performance of modern inference procedures post model selection or post shrinkage, particularly in situations where the number of parameters is larger than the sample size. As a technical contrib...
