5期

最新文章

• Estimation of extreme quantiles for functions of dependent random variables AI翻译
  作者：Gong, Jinguo; Li, Yadong; Peng, Liang; Yao, Qiwei
  作者单位：Southwestern University of Finance & Economics - China; Barclays; University System of Georgia; Georgia State University; University of London; London School Economics & Political Science; Peking University
  摘要：We propose a new method for estimating the extreme quantiles for a function of several dependent random variables. In contrast with the conventional approach based on extreme value theory, we do not impose the condition that the tail of the underlying distribution admits an approximate parametric form, and, furthermore, our estimation makes use of the full observed data. The method proposed is semiparametric as no parametric forms are assumed on the marginal distributions. But we select approp...
• 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...
• 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...
• A split-and-merge Bayesian variable selection approach for ultrahigh dimensional regression AI翻译
  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...