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作者：Meier, Lukas; van de Geer, Sara; Buehlmann, Peter
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：We propose a new sparsity-smoothness penalty for high-dimensional generalized additive models. The combination of sparsity and smoothness is crucial for mathematical theory as well as performance for finite-sample data. We present a computationally efficient algorithm, with provable numerical convergence properties, for optimizing the penalized likelihood. Furthermore, we provide oracle results which yield asymptotic optimality of our estimator for high dimensional but sparse additive models. ...
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作者：Andersson, Sofia; Ryden, Tobias
作者单位：AstraZeneca; Lund University
摘要：Hidden Markov models (HMMs) are probabilistic functions of finite Markov chains, or, put in other words, state space models with finite state space. In this paper, we examine subspace estimation methods for HMMs whose output lies a finite set as well. In particular, we study the geometric structure arising from the nonminimality of the linear state space representation of HMMs, and consistency of a subspace algorithm arising from a certain factorization of the singular value decomposition of t...
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作者：Dette, Holger; Holland-Letz, Tim
作者单位：Ruhr University Bochum; Ruhr University Bochum
摘要：We consider the common nonlinear regression model where the variance, as well as the mean, is a parametric function of the explanatory variables. The c-optimal design problem is investigated in the case when the parameters of both the mean and the variance function are of interest. A geometric characterization of c-optimal designs in this context is presented, which generalizes the classical result of Elfving [Ann. Math. Statist. 23 (1952) 255-262] for c-optimal designs. As in Elfving's famous...
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作者：Shi, Tao; Belkin, Mikhail; Yu, Bin
作者单位：University System of Ohio; Ohio State University; University System of Ohio; Ohio State University; University of California System; University of California Berkeley
摘要：This paper focuses on obtaining clustering information about a distribution from its i.i.d. samples. We develop theoretical results to understand and use clustering information contained in the eigenvectors of data adjacency matrices based on a radial kernel function with a sufficiently fast tail decay. In particular, we provide population analyses to gain insights into which eigenvectors should be used and when the clustering information for the distribution can be recovered from the sample. ...
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作者：Chan, Hock Peng
作者单位：National University of Singapore
摘要：Generalized likelihood ratio (GLR) test statistics are often used in the detection of spatial clustering in case-control and case-population datasets to check for a significantly large proportion of cases within some scanning window. The traditional spatial scan test statistic takes the supremum GLR value over all windows, whereas the average likelihood ratio (ALR) test statistic that we consider here takes an average of the GLR values. Numerical experiments in the literature and in this paper...
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作者：Gao, Jiti; King, Maxwell; Lu, Zudi; Tjostheim, Dag
作者单位：University of Adelaide; Monash University; Curtin University; University of Bergen
摘要：This paper considers a class of nonparametric autoregressive models with nonstationarity. We propose a nonparametric kernel test for the conditional mean and then establish an asymptotic distribution of the proposed test. Both the setting and the results differ from earlier work on nonparametric autoregression with stationarity. In addition, we develop a new bootstrap simulation scheme for the selection of a suitable bandwidth parameter involved in the kernel test as well as the choice of a si...
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作者：Lam, Clifford; Fan, Jianqing
作者单位：University of London; London School Economics & Political Science; Princeton University
摘要：This paper studies the sparsistency and rates of convergence for estimating sparse covariance and precision matrices based on penalized likelihood with nonconvex penalty functions. Here, sparsistency refers to the property that all parameters that are zero are actually estimated as zero with probability tending to one. Depending on the case of applications, sparsity priori may occur on the covariance matrix, its inverse or its Cholesky decomposition. We study these three sparsity exploration p...
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作者：Owen, Art B.
作者单位：Stanford University
摘要：This paper revisits a meta-analysis method proposed by Pearson [Biometrika 26 (1934) 425-442] and first used by David [Biometrika 26 (1934) 1-11]. It was thought to be inadmissible for over fifty years, dating back to a paper of Birnbaum [J. Amer Statist. Assoc. 49 (1954) 559-574]. It turns out that the method Birnbaum analyzed is not the one that Pearson proposed. We show that Pearson's proposal is admissible. Because it is admissible, it has better power than the standard test of Fisher [Sta...
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作者：Aue, Alexander; Hormann, Siegfried; Horvath, Lajos; Reimherr, Matthew
作者单位：University of California System; University of California Davis; Utah System of Higher Education; University of Utah; University of Chicago
摘要：In this paper, we introduce an asymptotic test procedure to assess the stability of volatilities and cross-volatilites of linear and nonlinear multivariate time series models, The test is very flexible as it can be applied, for example, to many of the multivariate GARCH models established in the literature, and also works well in the case of high dimensionality of the underlying data. Since it is nonparametric, the procedure avoids the difficulties associated with parametric model selection, m...
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作者：Song, Kyungchul
作者单位：University of Pennsylvania
摘要：This paper proposes new tests of conditional independence of two random variables given a single-index involving an unknown finite-dimensional parameter. The tests employ Rosenblatt transforms and are shown to be distribution-free while retaining computational convenience. Some results from Monte Carlo simulations are presented and discussed.
