Journal of the Royal Statistical Society: Series B

最新文章

• Quantile spectral analysis for locally stationary time series AI翻译
  作者：Birr, Stefan; Volgushev, Stanislav; Kley, Tobias; Dette, Holger; Hallin, Marc
  作者单位：Ruhr University Bochum; University of Toronto; University of London; London School Economics & Political Science; Universite Libre de Bruxelles
  摘要：Classical spectral methods are subject to two fundamental limitations: they can account only for covariance-related serial dependences, and they require second-order stationarity. Much attention has been devoted lately to quantile-based spectral methods that go beyond covariance-based serial dependence features. At the same time, covariance-based methods relaxing stationarity into much weaker local stationarity conditions have been developed for a variety of time series models. Here, we combin...
• Rank-based procedures in factorial designs: hypotheses about non-parametric treatment effects AI翻译
  作者：Brunner, Edgar; Konietschke, Frank; Pauly, Markus; Puri, Madan L.
  作者单位：University of Gottingen; University of Texas System; University of Texas Dallas; Ulm University; Indiana University System; Indiana University Bloomington
  摘要：Existing tests for factorial designs in the non-parametric case are based on hypotheses formulated in terms of distribution functions. Typical null hypotheses, however, are formulated in terms of some parameters or effect measures, particularly in heteroscedastic settings. Here this idea is extended to non-parametric models by introducing a novel non-parametric analysis-of-variance type of statistic based on ranks or pseudoranks which is suitable for testing hypotheses formulated in meaningful...
• Parameter stability and semiparametric inference in time varying auto-regressive conditional heteroscedasticity models AI翻译
  作者：Truquet, Lionel
  作者单位：Ecole Nationale de la Statistique et de l'Analyse de l'Information (ENSAI)
  摘要：We develop a complete methodology for detecting time varying or non-time-varying parameters in auto-regressive conditional heteroscedasticity (ARCH) processes. For this, we estimate and test various semiparametric versions of time varying ARCH models which include two well-known non-stationary ARCH-type models introduced in the econometrics literature. Using kernel estimation, we show that non-time-varying parameters can be estimated at the usual parametric rate of convergence and, for Gaussia...
• Testing against a linear regression model using ideas from shape-restricted estimation AI翻译
  作者：Sen, Bodhisattva; Meyer, Mary
  作者单位：Columbia University; Colorado State University System; Colorado State University Fort Collins
  摘要：A formal likelihood ratio hypothesis test for the validity of a parametric regression function is proposed, using a large dimensional, non-parametric double-cone alternative. For example, the test against a constant function uses the alternative of increasing or decreasing regression functions, and the test against a linear function uses the convex or concave alternative. The test proposed is exact and unbiased and the critical value is easily computed. The power of the test increases to 1 as ...
• A test for stationarity for irregularly spaced spatial data AI翻译
  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...
• Additive model building for spatial regression AI翻译
  作者：Nandy, Siddhartha; Lim, Chae Young; Maiti, Tapabrata
  作者单位：Michigan State University; Seoul National University (SNU)
  摘要：Spatial regression is an important predictive tool in many scientific applications and an additive model provides a flexible regression relationship between predictors and a response variable. We develop a regularized variable selection technique for building a spatial additive model. We find that the methods developed for independent data do not work well for spatially dependent data. This motivates us to propose a spatially weighted l2-error norm with a group lasso type of penalty to select ...
• Bayesian inference for Matern repulsive processes AI翻译
  作者：Rao, Vinayak; Adams, Ryan P.; Dunson, David D.
  作者单位：Purdue University System; Purdue University; Harvard University; Twitter, Inc.; Duke University
  摘要：In many applications involving point pattern data, the Poisson process assumption is unrealistic, with the data exhibiting a more regular spread. Such repulsion between events is exhibited by trees for example, because of competition for light and nutrients. Other examples include the locations of biological cells and cities, and the times of neuronal spikes. Given the many applications of repulsive point processes, there is a surprisingly limited literature developing flexible, realistic and ...
• A Bayesian information criterion for singular models AI翻译
  作者：Drton, Mathias; Plummer, Martyn
  作者单位：University of Washington; University of Washington Seattle; World Health Organization; International Agency for Research on Cancer (IARC)
  摘要：We consider approximate Bayesian model choice for model selection problems that involve models whose Fisher information matrices may fail to be invertible along other competing submodels. Such singular models do not obey the regularity conditions underlying the derivation of Schwarz's Bayesian information criterion BIC and the penalty structure in BIC generally does not reflect the frequentist large sample behaviour of the marginal likelihood. Although large sample theory for the marginal like...
• High dimensional semiparametric latent graphical model for mixed data AI翻译
  作者：Fan, Jianqing; Liu, Han; Ning, Yang; Zou, Hui
  作者单位：Princeton University; University of Minnesota System; University of Minnesota Twin Cities
  摘要：We propose a semiparametric latent Gaussian copula model for modelling mixed multivariate data, which contain a combination of both continuous and binary variables. The model assumes that the observed binary variables are obtained by dichotomizing latent variables that satisfy the Gaussian copula distribution. The goal is to infer the conditional independence relationship between the latent random variables, based on the observed mixed data. Our work has two main contributions: we propose a un...
• Probabilistic multi-resolution scanning for two-sample differences AI翻译
  作者：Soriano, Jacopo; Ma, Li
  作者单位：Duke University
  摘要：We propose a multi-resolution scanning approach to identifying two-sample differences. Windows of multiple scales are constructed through nested dyadic partitioning on the sample space and a hypothesis regarding the two-sample difference is defined on each window. Instead of testing the hypotheses on different windows independently, we adopt a joint graphical model, namely a Markov tree, on the null or alternative states of these hypotheses to incorporate spatial correlation across windows. Th...