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作者：Sigrist, Fabio; Kuensch, Hans R.; Stahel, Werner A.
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：Increasingly larger data sets of processes in space and time ask for statistical models and methods that can cope with such data. We show that the solution of a stochastic advection-diffusion partial differential equation provides a flexible model class for spatiotemporal processes which is computationally feasible also for large data sets. The Gaussian process defined through the stochastic partial differential equation has, in general, a non-separable covariance structure. Its parameters can...
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作者：Efron, Bradley
作者单位：Stanford University
摘要：In the absence of relevant prior experience, popular Bayesian estimation techniques usually begin with some form of uninformative' prior distribution intended to have minimal inferential influence. The Bayes rule will still produce nice looking estimates and credible intervals, but these lack the logical force that is attached to experience-based priors and require further justification. The paper concerns the frequentist assessment of Bayes estimates. A simple formula is shown to give the fre...
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作者：Fryzlewicz, Piotr; Van Keilegom, Ingrid
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作者：Schweinberger, Michael; Handcock, Mark S.
作者单位：Rice University; University of California System; University of California Los Angeles
摘要：Dependent phenomena, such as relational, spatial and temporal phenomena, tend to be characterized by local dependence in the sense that units which are close in a well-defined sense are dependent. In contrast with spatial and temporal phenomena, though, relational phenomena tend to lack a natural neighbourhood structure in the sense that it is unknown which units are close and thus dependent. Owing to the challenge of characterizing local dependence and constructing random graph models with lo...
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作者：Cho, Haeran; Fryzlewicz, Piotr
作者单位：University of Bristol; University of London; London School Economics & Political Science
摘要：Time series segmentation, which is also known as multiple-change-point detection, is a well-established problem. However, few solutions have been designed specifically for high dimensional situations. Our interest is in segmenting the second-order structure of a high dimensional time series. In a generic step of a binary segmentation algorithm for multivariate time series, one natural solution is to combine cumulative sum statistics obtained from local periodograms and cross-periodograms of th...
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作者：Hormann, Siegfried; Kidzinski, Lukasz; Hallin, Marc
作者单位：Universite Libre de Bruxelles; Princeton University
摘要：We address the problem of dimension reduction for time series of functional data (Xt:tZ). Such functional time series frequently arise, for example, when a continuous time process is segmented into some smaller natural units, such as days. Then each X-t represents one intraday curve. We argue that functional principal component analysis, though a key technique in the field and a benchmark for any competitor, does not provide an adequate dimension reduction in a time series setting. Functional ...
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作者：Song, Qifan; Liang, Faming
作者单位：Purdue University System; Purdue University; State University System of Florida; University of Florida
摘要：We propose a Bayesian variable selection approach for ultrahigh dimensional linear regression based on the strategy of split and merge. The approach proposed consists of two stages: split the ultrahigh dimensional data set into a number of lower dimensional subsets and select relevant variables from each of the subsets, and aggregate the variables selected from each subset and then select relevant variables from the aggregated data set. Since the approach proposed has an embarrassingly paralle...
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作者：Delaigle, Aurore; Hall, Peter; Jamshidi, Farshid
作者单位：University of Melbourne
摘要：Errors-in-variables regression is important in many areas of science and social science, e.g. in economics where it is often a feature of hedonic models, in environmental science where air quality indices are measured with error, in biology where the vegetative mass of plants is frequently obscured by mismeasurement and in nutrition where reported fat intake is typically subject to substantial error. To date, in non-parametric contexts, the great majority of work has focused on methods for est...
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作者：Guan, Yongtao; Jalilian, Abdollah; Waagepetersen, Rasmus
作者单位：University of Miami; Razi University; Aalborg University
摘要：Fitting regression models for intensity functions of spatial point processes is of great interest in ecological and epidemiological studies of association between spatially referenced events and geographical or environmental covariates. When Cox or cluster process models are used to accommodate clustering that is not accounted for by the available covariates, likelihoodbased inference becomes computationally cumbersome owing to the complicated nature of the likelihood function and the associat...
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作者：Dutta, Somak; Mondal, Debashis
作者单位：University of Chicago; Oregon State University
摘要：We consider sparse spatial mixed linear models, particularly those described by Besag and Higdon, and develop an h-likelihood method for their statistical inference. The method proposed allows for singular precision matrices, as it produces estimates that coincide with those from the residual maximum likelihood based on appropriate differencing of the data and has a novel approach to estimating precision parameters by a gamma linear model. Furthermore, we generalize the h-likelihood method to ...
