Journal of the Royal Statistical Society: Series B

最新文章

• A general dynamical statistical model with causal interpretation AI翻译
  作者：Commenges, Daniel; Gegout-Petit, Anne
  作者单位：Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Bordeaux; Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Bordeaux; Centre National de la Recherche Scientifique (CNRS); Inria; Universite de Bordeaux
  摘要：We develop a general dynamical model as a framework for causal interpretation. We first state a criterion of local independence in terms of measurability of processes that are involved in the Doob-Meyer decomposition of stochastic processes; then we define direct and indirect influence. We propose a definition of causal influence using the concepts of a 'physical system'. This framework makes it possible to link descriptive and explicative statistical models, and encompasses quantitative proce...
• Robust discrimination designs AI翻译
  作者：Wiens, Douglas P.
  作者单位：University of Alberta
  摘要：We study the construction of experimental designs, the purpose of which is to aid in the discrimination between two possibly non-linear regression models, each of which might be only approximately specified. A rough description of our approach is that we impose neighbourhood structures on each regression response and determine the members of these neighbourhoods which are least favourable in the sense of minimizing the Kullback-Leibler divergence. Designs are obtained which maximize this minim...
• A new class of models for bivariate joint tails AI翻译
  作者：Ramos, Alexandra; Ledford, Anthony
  作者单位：Universidade do Porto; University of Oxford
  摘要：A fundamental issue in applied multivariate extreme value analysis is modelling dependence within joint tail regions. The primary focus of this work is to extend the classical pseudopolar treatment of multivariate extremes to develop an asymptotically motivated representation of extremal dependence that also encompasses asymptotic independence. Starting with the usual mild bivariate regular variation assumptions that underpin the coefficient of tail dependence as a measure of extremal dependen...
• Pseudomartingale estimating equations for modulated renewal process models AI翻译
  作者：Lin, Fengchang; Fine, Jason P.
  作者单位：University of Wisconsin System; University of Wisconsin Madison
  摘要：We adapt martingale estimating equations based on gap time information to a general intensity model for a single realization of a modulated renewal process. The consistency and asymptotic normality of the estimators is proved under ergodicity conditions. Previous work has considered either parametric likelihood analysis or semiparametric multiplicative models using partial likelihood. The framework is generally applicable to semiparametric and parametric models, including additive and multipli...
• Causal inference in outcome-dependent two-phase sampling designs AI翻译
  作者：Wang, Weiwei; Scharfstein, Daniel; Tan, Zhiqiang; MacKenzie, Ellen J.
  作者单位：Princeton University; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Rutgers University System; Rutgers University New Brunswick
  摘要：We consider estimation of the causal effect of a treatment on an outcome from observational data collected in two phases. In the first phase, a simple random sample of individuals is drawn from a population. On these individuals, information is obtained on treatment, outcome and a few low dimensional covariates. These individuals are then stratified according to these factors. In the second phase, a random subsample of individuals is drawn from each stratum, with known stratum-specific selecti...
• Bayesian non-parametric inference for species variety with a two-parameter Poisson-Dirichlet process prior AI翻译
  作者：Favaro, Stefano; Lijoi, Antonio; Mena, Ramses H.; Prunster, Igor
  作者单位：Universidad Nacional Autonoma de Mexico; Collegio Carlo Alberto; University of Turin; University of Pavia; Consiglio Nazionale delle Ricerche (CNR); Istituto di Matematica Applicata e Tecnologie Informatiche Enrico Magenes (IMATI-CNR)
  摘要：A Bayesian non-parametric methodology has been recently proposed to deal with the issue of prediction within species sampling problems. Such problems concern the evaluation, conditional on a sample of size n, of the species variety featured by an additional sample of size m. Genomic applications pose the additional challenge of having to deal with large values of both n and m. In such a case the computation of the Bayesian non-parametric estimators is cumbersome and prevents their implementati...
• Fourier analysis of irregularly spaced data on Rd AI翻译
  作者：Matsuda, Yasumasa; Yajima, Yoshihiro
  作者单位：Tohoku University; University of Tokyo
  摘要：The purpose of the paper is to propose a frequency domain approach for irregularly spaced data on R-d. We extend the original definition of a periodogram for time series to that for irregularly spaced data and define non-parametric and parametric spectral density estimators in a way that is similar to the classical approach. Introduction of the mixed asymptotics, which are one of the asymptotics for irregularly spaced data, makes it possible to provide asymptotic theories to the spectral estim...
• Two-step estimation for inhomogeneous spatial point processes AI翻译
  作者：Waagepetersen, Rasmus; Guan, Yongtao
  作者单位：Aalborg University; Yale University
  摘要：The paper is concerned with parameter estimation for inhomogeneous spatial point processes with a regression model for the intensity function and tractable second-order properties (K-function). Regression parameters are estimated by using a Poisson likelihood score estimating function and in the second step minimum contrast estimation is applied for the residual clustering parameters. Asymptotic normality of parameter estimates is established under certain mixing conditions and we exemplify ho...
• Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations AI翻译
  作者：Rue, Havard; Martino, Sara; Chopin, Nicolas
  作者单位：Norwegian University of Science & Technology (NTNU); Institut Polytechnique de Paris; ENSAE Paris; Institut Polytechnique de Paris; ENSAE Paris
  摘要：Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalized) linear models, (generalized) additive models, smoothing spline models, state space models, semiparametric regression, spatial and spatiotemporal models, log-Gaussian Cox processes and geostatistical and geoadditive models. We consider approximate Bayesian inference in a popular subset of structured additive regression models, latent Gaus...
• Bootstrapping frequency domain tests in multivariate time series with an application to comparing spectral densities AI翻译
  作者：Dette, Holger; Paparoditis, Efstathios
  作者单位：Ruhr University Bochum; University of Cyprus
  摘要：We propose a general bootstrap procedure to approximate the null distribution of non-parametric frequency domain tests about the spectral density matrix of a multivariate time series. Under a set of easy-to-verify conditions, we establish asymptotic validity of the bootstrap procedure proposed. We apply a version of this procedure together with a new statistic to test the hypothesis that the spectral densities of not necessarily independent time series are equal. The test statistic proposed is...