2009

最新文章

• 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...
• Sparse additive models AI翻译
  作者：Ravikumar, Pradeep; Lafferty, John; Liu, Han; Wasserman, Larry
  作者单位：Carnegie Mellon University; University of California System; University of California Berkeley
  摘要：We present a new class of methods for high dimensional non-parametric regression and classification called sparse additive models. Our methods combine ideas from sparse linear modelling and additive non-parametric regression. We derive an algorithm for fitting the models that is practical and effective even when the number of covariates is larger than the sample size. Sparse additive models are essentially a functional version of the grouped lasso of Yuan and Lin. They are also closely related...
• Fully exponential Laplace approximations for the joint modelling of survival and longitudinal data AI翻译
  作者：Rizopoulos, Dimitris; Verbeke, Geert; Lesaffre, Emmanuel
  作者单位：Erasmus University Rotterdam; Erasmus MC; KU Leuven; Hasselt University
  摘要：A common objective in longitudinal studies is the joint modelling of a longitudinal response with a time-to-event outcome. Random effects are typically used in the joint modelling framework to explain the interrelationships between these two processes. However, estimation in the presence of random effects involves intractable integrals requiring numerical integration. We propose a new computational approach for fitting such models that is based on the Laplace method for integrals that makes th...
• DASSO: connections between the Dantzig selector and lasso AI翻译
  作者：James, Gareth M.; Radchenko, Peter; Lv, Jinchi
  作者单位：University of Southern California
  摘要：We propose a new algorithm, DASSO, for fitting the entire coefficient path of the Dantzig selector with a similar computational cost to the least angle regression algorithm that is used to compute the lasso. DASSO efficiently constructs a piecewise linear path through a sequential simplex-like algorithm, which is remarkably similar to the least angle regression algorithm. Comparison of the two algorithms sheds new light on the question of how the lasso and Dantzig selector are related. In addi...
• Smoothing parameter selection for a class of semiparametric linear models AI翻译
  作者：Reiss, Philip T.; Ogden, R. Todd
  作者单位：New York University; Nathan Kline Institute for Psychiatric Research; Columbia University
  摘要：Spline-based approaches to non-parametric and semiparametric regression, as well as to regression of scalar outcomes on functional predictors, entail choosing a parameter controlling the extent to which roughness of the fitted function is penalized. We demonstrate that the equations determining two popular methods for smoothing parameter selection, generalized cross-validation and restricted maximum likelihood, share a similar form that allows us to prove several results which are common to bo...
• Finding an unknown number of multivariate outliers AI翻译
  作者：Riani, Marco; Atkinson, Anthony C.; Cerioli, Andrea
  作者单位：University of London; London School Economics & Political Science; University of Parma
  摘要：We use the forward search to provide robust Mahalanobis distances to detect the presence of outliers in a sample of multivariate normal data. Theoretical results on order statistics and on estimation in truncated samples provide the distribution of our test statistic. We also introduce several new robust distances with associated distributional results. Comparisons of our procedure with tests using other robust Mahalanobis distances show the good size and high power of our procedure. We also p...