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作者：Zhang, Nancy R.; Siegmund, David O.; Ji, Hanlee; Li, Jun Z.
作者单位：Stanford University; Stanford University; University of Michigan System; University of Michigan
摘要：We discuss the detection of local signals that occur at the same location in multiple one-dimensional noisy sequences, with particular attention to relatively weak signals that may occur in only a fraction of the sequences. We propose simple scan and segmentation algorithms based on the sum of the chi-squared statistics for each individual sample, which is equivalent to the generalized likelihood ratio for a model where the errors in each sample are independent. The simple geometry of the stat...
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作者：Wang, Suojin; Qian, Lianfen; Carroll, Raymond J.
作者单位：Texas A&M University System; Texas A&M University College Station; State University System of Florida; Florida Atlantic University
摘要：Efficient estimation of parameters is a major objective in analyzing longitudinal data. We propose two generalized empirical likelihood-based methods that take into consideration within-subject correlations. A nonparametric version of the Wilks theorem for the limiting distributions of the empirical likelihood ratios is derived. It is shown that one of the proposed methods is locally efficient among a class of within-subject variance-covariance matrices. A simulation study is conducted to inve...
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作者：Chatterjee, Nilanjan; Sinha, Samiran; Diver, W. Ryan; Feigelson, Heather Spencer
作者单位：National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics; Texas A&M University System; Texas A&M University College Station; American Cancer Society
摘要：Complex diseases like cancers can often be classified into subtypes using various pathological and molecular traits of the disease. In this article, we develop methods for analysis of disease incidence in cohort studies incorporating data on multiple disease traits using a two-stage semiparametric Cox proportional hazards regression model that allows one to examine the heterogeneity in the effect of the covariates by the levels of the different disease traits. For inference in the presence of ...
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作者：Kenobi, Kim; Dryden, Ian L.; Le, Huiling
作者单位：University of Nottingham; University of Nottingham
摘要：A family of shape curves is introduced that is useful for modelling the changes in shape in a series of geometrical objects. The relationship between the preshape sphere and the shape space is used to define a general family of curves based on horizontal geodesics on the preshape sphere. Methods for fitting geodesics and more general curves in the non-Euclidean shape space of point sets are discussed, based on minimizing sums of squares of Procrustes distances. Likelihood-based inference is co...
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作者：Zhu, Liping; Wang, Tao; Zhu, Lixing; Ferre, Louis
作者单位：East China Normal University; Hong Kong Baptist University; Universite Federale Toulouse Midi-Pyrenees (ComUE); Universite de Toulouse; Institut National des Sciences Appliquees de Toulouse; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Universite Toulouse III - Paul Sabatier
摘要：In the context of sufficient dimension reduction, the goal is to parsimoniously recover the central subspace of a regression model. Many inverse regression methods use slicing estimation to recover the central subspace. The efficacy of slicing estimation depends heavily upon the number of slices. However, the selection of the number of slices is an open and long-standing problem. In this paper, we propose a discretization-expectation estimation method, which avoids selecting the number of slic...
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作者：Chen, Xin; Cook, R. Dennis
作者单位：University of Minnesota System; University of Minnesota Twin Cities
摘要：Continuum regression encompasses ordinary least squares regression, partial least squares regression and principal component regression under the same umbrella using a nonnegative parameter gamma. However, there seems to be no literature discussing the asymptotic properties for arbitrary continuum regression parameter gamma. This article establishes a relation between continuum regression and sufficient dimension reduction and studies the asymptotic properties of continuum regression for arbit...
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作者：Brockwell, Peter J.; Lindner, Alexander
作者单位：Colorado State University System; Colorado State University Fort Collins; Braunschweig University of Technology
摘要：Necessary and sufficient conditions for the existence of a strictly stationary solution of the equations defining an autoregressive moving average process driven by an independent and identically distributed noise sequence are determined. No moment assumptions on the driving noise sequence are made.
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作者：Reid, N.; Fraser, D. A. S.
作者单位：University of Toronto
摘要：Higher-order approximations to p-values can be obtained from the loglikelihood function and a reparameterization that can be viewed as a canonical parameter in an exponential family approximation to the model. This approach clarifies the connection between Skovgaard (1996) and Fraser et al. (1999a), and shows that the Skovgaard approximation can be obtained directly using the mean loglikelihood function.
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作者：Buehlmann, P.; Kalisch, M.; Maathuis, M. H.
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：We consider variable selection in high-dimensional linear models where the number of covariates greatly exceeds the sample size. We introduce the new concept of partial faithfulness and use it to infer associations between the covariates and the response. Under partial faithfulness, we develop a simplified version of the PC algorithm (Spirtes et al., 2000), which is computationally feasible even with thousands of covariates and provides consistent variable selection under conditions on the ran...
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作者：Zheng, Yanbing; Zhu, Jun; Roy, Anindya
作者单位：University of Kentucky; University of Wisconsin System; University of Wisconsin Madison; University System of Maryland; University of Maryland Baltimore County
摘要：A powerful technique for inference concerning spatial dependence in a random field is to use spectral methods based on frequency domain analysis. Here we develop a nonparametric Bayesian approach to statistical inference for the spectral density of a random field. We construct a multi-dimensional Bernstein polynomial prior for the spectral density and devise a Markov chain Monte Carlo algorithm to simulate from the posterior of the spectral density. The posterior sampling enables us to obtain ...
