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作者：Vogt, Michael; Linton, Oliver
作者单位：University of Bonn; University of Cambridge
摘要：We investigate a longitudinal data model with non-parametric regression functions that may vary across the observed individuals. In a variety of applications, it is natural to impose a group structure on the regression curves. Specifically, we may suppose that the observed individuals can be grouped into a number of classes whose members all share the same regression function. We develop a statistical procedure to estimate the unknown group structure from the data. Moreover, we derive the asym...
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作者：Chaudhuri, Sanjay; Mondal, Debashis; Yin, Teng
作者单位：National University of Singapore; Oregon State University
摘要：We consider Bayesian empirical likelihood estimation and develop an efficient Hamiltonian Monte Carlo method for sampling from the posterior distribution of the parameters of interest. The method proposed uses hitherto unknown properties of the gradient of the underlying log-empirical-likelihood function. We use results from convex analysis to show that these properties hold under minimal assumptions on the parameter space, prior density and the functions used in the estimating equations deter...
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作者：Bandyopadhyay, Soutir; Rao, Suhasini Subba
作者单位：Lehigh University; Texas A&M University System; Texas A&M University College Station
摘要：The analysis of spatial data is based on a set of assumptions, which in practice need to be checked. A commonly used assumption is that the spatial random field is second-order stationary. In the paper, a test for spatial stationarity for irregularly sampled data is proposed. The test is based on a transformation of the data (a type of Fourier transform), where the correlations between the transformed data are close to 0 if the random field is second-order stationary. However, if the random fi...
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作者：Fan, Jianqing; Li, Quefeng; Wang, Yuyan
作者单位：Princeton University; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; University of North Carolina; University of North Carolina Chapel Hill; Princeton University
摘要：Data subject to heavy-tailed errors are commonly encountered in various scientific fields. To address this problem, procedures based on quantile regression and least absolute deviation regression have been developed in recent years. These methods essentially estimate the conditional median (or quantile) function. They can be very different from the conditional mean functions, especially when distributions are asymmetric and heteroscedastic. How can we efficiently estimate the mean regression f...
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作者：Dunson, David; Fryzlewicz, Piotr
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作者：Gromenko, Oleksandr; Kokoszka, Piotr; Reimherr, Matthew
作者单位：Tulane University; Colorado State University System; Colorado State University Fort Collins; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：The paper develops inferential methodology for detecting a change in the annual pattern of an environmental variable measured at fixed locations in a spatial region. Using a framework built on functional data analysis, we model observations as a collection of function-valued time sequences available at many sites. Each sequence is modelled as an annual mean function, which may change, plus a sequence of error functions, which are spatially correlated. The tests statistics extend the cumulative...
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作者：Wadsworth, J. L.; Tawn, J. A.; Davison, A. C.; Elton, D. M.
作者单位：Lancaster University; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
摘要：Different dependence scenarios can arise in multivariate extremes, entailing careful selection of an appropriate class of models. In bivariate extremes, the variables are either asymptotically dependent or are asymptotically independent. Most available statistical models suit one or other of these cases, but not both, resulting in a stage in the inference that is unaccounted for but can substantially impact subsequent extrapolation. Existing modelling solutions to this problem are either appli...
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作者：Comte, Fabienne; Cuenod, Charles-A.; Pensky, Marianna; Rozenholc, Yves
作者单位：Universite Paris Cite; Assistance Publique Hopitaux Paris (APHP); Universite Paris Cite; Hopital Universitaire Europeen Georges-Pompidou - APHP; State University System of Florida; University of Central Florida
摘要：We consider the problem of Laplace deconvolution with noisy discrete non-equally spaced observations on a finite time interval. We propose a new method for Laplace deconvolution which is based on expansions of the convolution kernel, the unknown function and the observed signal over a Laguerre functions basis (which acts as a surrogate eigenfunction basis of the Laplace convolution operator) using a regression setting. The expansion results in a small system of linear equations with the matrix...
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作者：Stadler, Nicolas; Mukherjee, Sach
作者单位：Netherlands Cancer Institute; Helmholtz Association; German Center for Neurodegenerative Diseases (DZNE)
摘要：We propose new methodology for two-sample testing in high dimensional models. The methodology provides a high dimensional analogue to the classical likelihood ratio test and is applicable to essentially any model class where sparse estimation is feasible. Sparse structure is used in the construction of the test statistic. In the general case, testing then involves non-nested model comparison, and we provide asymptotic results for the high dimensional setting. We put forward computationally eff...
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作者：Botev, Z. I.
作者单位：University of New South Wales Sydney
摘要：Simulation from the truncated multivariate normal distribution in high dimensions is a recurrent problem in statistical computing and is typically only feasible by using approximate Markov chain Monte Carlo sampling. We propose a minimax tilting method for exact independently and identically distributed data simulation from the truncated multivariate normal distribution. The new methodology provides both a method for simulation and an efficient estimator to hitherto intractable Gaussian integr...
