Journal of the Royal Statistical Society: Series B

最新文章

• Quantile regression adjusting for dependent censoring from semicompeting risks AI翻译
  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...
• Semiparametric transformation models for causal inference in time-to-event studies with all-or-nothing compliance AI翻译
  作者：Yu, Wen; Chen, Kani; Sobel, Michael E.; Ying, Zhiliang
  作者单位：Fudan University; Hong Kong University of Science & Technology; Columbia University
  摘要：We consider causal inference in randomized survival studies with right-censored outcomes and all-or-nothing compliance, using semiparametric transformation models to estimate the distribution of survival times in treatment and control groups, conditionally on covariates and latent compliance type. Estimands depending on these distributions, e.g. the complier average causal effect, the complier effect on survival beyond time t and the complier quantile effect, are then considered. Maximum likel...
• Marginally specified priors for non-parametric Bayesian estimation AI翻译
  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...
• Bootstrapping locally stationary processes AI翻译
  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...
• False discovery control in large-scale spatial multiple testing AI翻译
  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...
• Group bound: confidence intervals for groups of variables in sparse high dimensional regression without assumptions on the design AI翻译
  作者：Meinshausen, Nicolai
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：It is in general challenging to provide confidence intervals for individual variables in high dimensional regression without making strict or unverifiable assumptions on the design matrix. We show here that a 'group bound' confidence interval can be derived without making any assumptions on the design matrix. The lower bound for the regression coefficient of individual variables can be derived via linear programming. The idea also generalizes naturally to groups of variables, where we can deri...
• Bayesian non-parametric models for spatially indexed data of mixed type AI翻译
  作者：Papageorgiou, Georgios; Richardson, Sylvia; Best, Nicky
  作者单位：University of London; MRC Biostatistics Unit; Imperial College London
  摘要：We develop Bayesian non-parametric models for spatially indexed data of mixed type. Our work is motivated by challenges that occur in environmental epidemiology, where the usual presence of several confounding variables that exhibit complex interactions and high correlations makes it difficult to estimate and understand the effects of risk factors on health outcomes of interest. The modelling approach that we adopt assumes that responses and confounding variables are manifestations of continuo...
• Joint modelling of repeated measurements and time-to-event outcomes: flexible model specification and exact likelihood inference AI翻译
  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...
• Estimation of Husler-Reiss distributions and Brown-Resnick processes AI翻译
  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...
• A new non-parametric stationarity test of time series in the time domain AI翻译
  作者：Jin, Lei; Wang, Suojin; Wang, Haiyan
  作者单位：Texas A&M University System; Texas A&M University System; Texas A&M University College Station; Kansas State University
  摘要：We propose a new double-order selection test for checking second-order stationarity of a time series. To develop the test, a sequence of systematic samples is defined via Walsh functions. Then the deviations of the autocovariances based on these systematic samples from the corresponding autocovariances of the whole time series are calculated and the uniform asymptotic joint normality of these deviations over different systematic samples is obtained. With a double-order selection scheme, our te...