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作者：Ward, S.; Battey, H. S.; Cohen, E. A. K.
作者单位：Imperial College London
摘要：This paper is concerned with nonparametric estimation of the intensity function of a point process on a Riemannian manifold. It provides a first-order asymptotic analysis of the proposed kernel estimator for Poisson processes, supplemented by empirical work to probe the behaviour in finite samples and under other generative regimes. The investigation highlights the scope for finite-sample improvements by allowing the bandwidth to adapt to local curvature.
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作者：Rosenbaum, P. R.; Rubin, D. B.
作者单位：University of Pennsylvania; Harvard University
摘要：The design of any study, whether experimental or observational, that is intended to estimate the causal effects of a treatment condition relative to a control condition refers to those activities that precede any examination of outcome variables. As defined in our 1983 article (), the propensity score is the unit-level conditional probability of assignment to treatment versus control given the observed covariates; so the propensity score explicitly does not involve any outcome variables, in co...
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作者：Chen, Jinsong; Li, Quefeng; Chen, Hua Yun
作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; University of North Carolina; University of North Carolina Chapel Hill; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
摘要：Generalized linear models often have high-dimensional nuisance parameters, as seen in applications such as testing gene-environment interactions or gene-gene interactions. In these scenarios, it is essential to test the significance of a high-dimensional subvector of the model's coefficients. Although some existing methods can tackle this problem, they often rely on the bootstrap to approximate the asymptotic distribution of the test statistic, and are thus computationally expensive. Here, we ...
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作者：Lin, Z.; Muller, H. G.; Park, B. U.
作者单位：National University of Singapore; University of California System; University of California Davis; Seoul National University (SNU)
摘要：We propose and investigate an additive regression model for symmetric positive-definite matrix-valued responses and multiple scalar predictors. The model exploits the Abelian group structure inherited from either of the log-Cholesky and log-Euclidean frameworks for symmetric positive-definite matrices and naturally extends to general Abelian Lie groups. The proposed additive model is shown to connect to an additive model on a tangent space. This connection not only entails an efficient algorit...
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作者：Lu, Zitong; Geng, Zhi; Li, Wei; Zhu, Shengyu; Jia, Jinzhu
作者单位：Peking University; Beijing Technology & Business University; Renmin University of China; Renmin University of China; Huawei Technologies; Peking University; Peking University
摘要：For the case with a single causal variable, Dawid et al. (2014) defined the probability of causation, and Pearl (2000) defined the probability of necessity to assess the causes of effects. For a case with multiple causes that could affect each other, this paper defines the posterior total and direct causal effects based on the evidence observed for post-treatment variables, which could be viewed as measurements of causes of effects. Posterior causal effects involve the probabilities of counter...
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作者：Yu, Long; Xie, Jiahui; Zhou, Wang
作者单位：Shanghai University of Finance & Economics; National University of Singapore
摘要：The Kronecker product covariance structure provides an efficient way to model the inter-correlations of matrix-variate data. In this paper, we propose test statistics for the Kronecker product covariance matrix based on linear spectral statistics of renormalized sample covariance matrices. A central limit theorem is proved for the linear spectral statistics, with explicit formulas for the mean and covariance functions, thereby filling a gap in the literature. We then show theoretically that th...
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作者：Kovacs, S.; Buehlmann, P.; Li, H.; Munk, A.
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Gottingen
摘要：We propose seeded binary segmentation for large-scale changepoint detection problems. We construct a deterministic set of background intervals, called seeded intervals, in which single changepoint candidates are searched for. The final selection of changepoints based on these candidates can be done in various ways, adapted to the problem at hand. The method is thus easy to adapt to many changepoint problems, ranging from univariate to high dimensional. Compared to recently popular random backg...
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作者：Hu, Jianhua; Huang, Jian; Liu, Xiaoqian; Liu, Xu
作者单位：Shanghai University of Finance & Economics; Hong Kong Polytechnic University; York University - Canada
摘要：This article investigates the statistical problem of response-variable selection with high-dimensional response variables and a diverging number of predictor variables with respect to the sample size in the framework of multivariate linear regression. A response best-subset selection model is proposed by introducing a 0-1 selection indicator for each response variable, and then a response best-subset selector is developed by introducing a separation parameter and a novel penalized least-square...
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作者：Koning, N. W.; Hemerik, J.
作者单位：Erasmus University Rotterdam; Erasmus University Rotterdam - Excl Erasmus MC; Wageningen University & Research
摘要：We consider testing invariance of a distribution under an algebraic group of transformations, such as permutations or sign flips. As such groups are typically huge, tests based on the full group are often computationally infeasible. Hence, it is standard practice to use a random subset of transformations. We improve upon this by replacing the random subset with a strategically chosen, fixed subgroup of transformations. In a generalized location model, we show that the resulting tests are often...
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作者：Mccormack, A.; Hoff, P. D.
作者单位：Duke University
摘要：The Frechet mean generalizes the concept of a mean to a metric space setting. In this work we consider equivariant estimation of Frechet means for parametric models on metric spaces that are Riemannian manifolds. The geometry and symmetry of such a space are partially encoded by its isometry group of distance-preserving transformations. Estimators that are equivariant under the isometry group take into account the symmetry of the metric space. For some models, there exists an optimal equivaria...
