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作者：Martin, Ryan; Liu, Chuanhai
作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Purdue University System; Purdue University
摘要：The inferential model (IM) framework provides valid prior-free probabilistic inference by focusing on predicting unobserved auxiliary variables. But, efficient IM-based inference can be challenging when the auxiliary variable is of higher dimension than the parameter. Here we show that features of the auxiliary variable are often fully observed and, in such cases, a simultaneous dimension reduction and information aggregation can be achieved by conditioning. This proposed conditioning strategy...
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作者：Li, Ruosha; Peng, Limin
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Emory University
摘要：We study quantile regression when the response is an event time subject to potentially dependent censoring. We consider the semicompeting risks setting, where the time to censoring remains observable after the occurrence of the event of interest. Although such a scenario frequently arises in biomedical studies, most of current quantile regression methods for censored data are not applicable because they generally require the censoring time and the event time to be independent. By imposing quit...
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作者：Kessler, David C.; Hoff, Peter D.; Dunson, David B.
作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of Washington; University of Washington Seattle; Duke University
摘要：Prior specification for non-parametric Bayesian inference involves the difficult task of quantifying prior knowledge about a parameter of high, often infinite, dimension. A statistician is unlikely to have informed opinions about all aspects of such a parameter but will have real information about functionals of the parameter, such as the population mean or variance. The paper proposes a new framework for non-parametric Bayes inference in which the prior distribution for a possibly infinite di...
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作者：Kreiss, Jens-Peter; Paparoditis, Efstathios
作者单位：Braunschweig University of Technology; University of Cyprus
摘要：We propose a non-parametric method to bootstrap locally stationary processes which combines a time domain wild bootstrap approach with a non-parametric frequency domain approach. The method generates pseudotime series which mimic (asymptotically) correct, the local second-and to the necessary extent the fourth-order moment structure of the underlying process. Thus it can be applied to approximate the distribution of several statistics that are based on observations of the locally stationary pr...
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作者：Sun, Wenguang; Reich, Brian J.; Cai, T. Tony; Guindani, Michele; Schwartzman, Armin
作者单位：University of Southern California; North Carolina State University; University of Pennsylvania; University of Texas System; UTMD Anderson Cancer Center
摘要：The paper develops a unified theoretical and computational framework for false discovery control in multiple testing of spatial signals. We consider both pointwise and clusterwise spatial analyses, and derive oracle procedures which optimally control the false discovery rate, false discovery exceedance and false cluster rate. A data-driven finite approximation strategy is developed to mimic the oracle procedures on a continuous spatial domain. Our multiple-testing procedures are asymptotically...
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作者：Barrett, Jessica; Diggle, Peter; Henderson, Robin; Taylor-Robinson, David
作者单位：University of Cambridge; Lancaster University; University of Liverpool; Newcastle University - UK
摘要：Random effects or shared parameter models are commonly advocated for the analysis of combined repeated measurement and event history data, including dropout from longitudinal trials. Their use in practical applications has generally been limited by computational cost and complexity, meaning that only simple special cases can be fitted by using readily available software. We propose a new approach that exploits recent distributional results for the extended skew normal family to allow exact lik...
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作者：Engelke, Sebastian; Malinowski, Alexander; Kabluchko, Zakhar; Schlather, Martin
作者单位：University of Lausanne; University of Gottingen; University of Mannheim; Ulm University
摘要：Estimation of extreme value parameters from observations in the max-domain of attraction of a multivariate max-stable distribution commonly uses aggregated data such as block maxima. Multivariate peaks-over-threshold methods, in contrast, exploit additional information from the non-aggregated 'large' observations. We introduce an approach based on peaks over thresholds that provides several new estimators for processes eta in the max-domain of attraction of the frequently used Husler-Reiss mod...
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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：Fryzlewicz, Piotr; Van Keilegom, Ingrid
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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...
