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作者：Zhong, Ping-Shou; Li, Runze; Santo, Shawn
作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Michigan State University
摘要：This paper deals with the detection and identification of changepoints among covariances of high-dimensional longitudinal data, where the number of features is greater than both the sample size and the number of repeated measurements. The proposed methods are applicable under general temporal-spatial dependence. A new test statistic is introduced for changepoint detection, and its asymptotic distribution is established. If a changepoint is detected, an estimate of the location is provided. The...
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作者：Livingstone, S.; Faulkner, M. F.; Roberts, G. O.
作者单位：University of Bristol; University of Warwick
摘要：We consider how different choices of kinetic energy in Hamiltonian Monte Carlo affect algorithm performance. To this end, we introduce two quantities which can be easily evaluated, the composite gradient and the implicit noise. Results are established on integrator stability and geometric convergence, and we show that choices of kinetic energy that result in heavy-tailed momentum distributions can exhibit an undesirable negligible moves property, which we define. A general efficiency-robustnes...
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作者：Kraus, David; Stefanucci, Marco
作者单位：Masaryk University; Sapienza University Rome
摘要：We consider classification of functional data into two groups by linear classifiers based on one-dimensional projections of functions. We reformulate the task of finding the best classifier as an optimization problem and solve it by the conjugate gradient method with early stopping, the principal component method, and the ridge method. We study the empirical version with finite training samples consisting of incomplete functions observed on different subsets of the domain and show that the opt...
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作者：Lee, D.; Kim, J. K.; Skinner, C. J.
作者单位：Iowa State University; University of London; London School Economics & Political Science
摘要：A within-cluster resampling method is proposed for fitting a multilevel model in the presence of informative cluster size. Our method is based on the idea of removing the information in the cluster sizes by drawing bootstrap samples which contain a fixed number of observations from each cluster. We then estimate the parameters by maximizing an average, over the bootstrap samples, of a suitable composite loglikelihood. The consistency of the proposed estimator is shown and does not require that...
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作者：Yang, S.; Wang, L.; Ding, P.
作者单位：North Carolina State University; University of Toronto; University of California System; University of California Berkeley
摘要：It is important to draw causal inference from observational studies, but this becomes challenging if the confounders have missing values. Generally, causal effects are not identifiable if the confounders are missing not at random. In this article we propose a novel framework for non-parametric identification of causal effects with confounders subject to an outcome-independent missingness, which means that the missing data mechanism is independent of the outcome, given the treatment and possibl...
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作者：Krieger, A. M.; Azriel, D.; Kapelner, A.
作者单位：University of Pennsylvania; Technion Israel Institute of Technology; City University of New York (CUNY) System; Queens College NY (CUNY)
摘要：We present a procedure that divides a set of experimental units into two groups that are similar on a prespecified set of covariates and are almost as random as with a complete randomization. Under complete randomization, the difference in the standardized average between treatment and control is O-p(n(-1/2)), which may be material in small samples. We present an algorithm that reduces imbalance to O-p(n(-3)) for one covariate and O-p{n(-(1+2/p))} for p covariates, but whose assignments are, s...
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作者：Guinness, Joseph
作者单位：Cornell University
摘要：We introduce methods for estimating the spectral density of a random field on a d-dimensional lattice from incomplete gridded data. Data are iteratively imputed onto an expanded lattice according to a model with a periodic covariance function. The imputations are convenient computationally, in that circulant embedding and preconditioned conjugate gradient methods can produce imputations in O(n log n) time and O(n) memory. However, these so-called periodic imputations are motivated mainly by th...
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作者：Lyddon, S. P.; Holmes, C. C.; Walker, S. G.
作者单位：University of Oxford; University of Texas System; University of Texas Austin
摘要：In this paper we revisit the weighted likelihood bootstrap, a method that generates samples from an approximate Bayesian posterior of a parametric model. We show that the same method can be derived, without approximation, under a Bayesian nonparametric model with the parameter of interest defined through minimizing an expected negative loglikelihood under an unknown sampling distribution. This interpretation enables us to extend the weighted likelihood bootstrap to posterior sampling for param...
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作者：Engelke, Sebastian; De Fondeville, Raphael; Oesting, Marco
作者单位：University of Geneva; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Universitat Siegen
摘要：The distribution of spatially aggregated data from a stochastic process may exhibit tail behaviour different from that of its marginal distributions. For a large class of aggregating functionals we introduce the -extremal coefficient, which quantifies this difference as a function of the extremal spatial dependence in . We also obtain the joint extremal dependence for multiple aggregation functionals applied to the same process. Formulae for the -extremal coefficients and multivariate dependen...
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作者：Cape, J.; Tang, M.; Priebe, C. E.
作者单位：Johns Hopkins University
摘要：Estimating eigenvectors and low-dimensional subspaces is of central importance for numerous problems in statistics, computer science and applied mathematics. In this paper we characterize the behaviour of perturbed eigenvectors for a range of signal-plus-noise matrix models encountered in statistical and random-matrix-theoretic settings. We establish both first-order approximation results, i.e., sharp deviations, and second-order distributional limit theory, i.e., fluctuations. The concise met...
