2001

最新文章

• Bayesian wavelet regression on curves with application to a spectroscopic calibration problem AI翻译
  作者：Brown, PJ; Fearn, T; Vannucci, M
  作者单位：University of Kent; University of London; University College London; Texas A&M University System; Texas A&M University College Station
  摘要：Motivated by calibration problems in near-infrared (NIR) spectroscopy we consider the linear regression setting in which the many predictor variables arise from sampling an essentially continuous curve at equally spaced points and there may be multiple predictands. We tackle this regression problem by calculating the wavelet transforms of the discretized curves. then applying a Bayesian variable selection method using mixture priors to the multivariate regression of predictands on wavelet coef...
• Monte Carlo deconvolution of digital signals guided by the inverse filter AI翻译
  作者：Li, KC; Shedden, K
  作者单位：University of California System; University of California Los Angeles; University of Michigan System; University of Michigan
  摘要：Digital deconvolution concerns the restoration of an underlying discrete signal from a blurred noisy observation sequence. The problem can be formulated in a Bayesian framework. As is usual in the Bayesian context, the computation of relevant posterior quantities is the major challenge. Previous work made substantial progress toward making this computation feasible, most notably through the Gibbs sampling approach of Chen and Li and the sequential importance sampling approach of Liu and Chen. ...
• Bayesian forecasting for complex systems using computer simulators AI翻译
  作者：Craig, PS; Goldstein, M; Rougier, JC; Seheult, AH
  作者单位：Durham University
  摘要：Although computer models are often used for forecasting future outcomes of complex systems, the uncertainties in such forecasts are not usually treated formally. We describe a general Bayesian approach for using a computer model or simulator of a complex system to forecast system outcomes. The approach is based on constructing beliefs derived from a combination of expert judgments and experiments on the computer model. These beliefs, which are systematically updated as we make runs of the comp...
• Smoothing spline ANOVA for multivariate Bernoulli observations, with application to ophthalmology data - Comment AI翻译
  作者：Aerts, M; Molenberghs, G
  作者单位：Hasselt University
• Objective Bayesian analysis of spatially correlated data AI翻译
  作者：Berger, JO; De Oliveira, V; Sansó, B
  作者单位：Duke University; Simon Bolivar University
  摘要：Spatially varying phenomena are often modeled using Gaussian random fields, specified by their mean function and covariance function. The spatial correlation structure of these models is commonly specified to be of a certain form (e.g., spherical, power exponential, rational quadratic, or Matern) with a small number of unknown parameters. We consider objective Bayesian analysis of such spatial models, when the mean function of the Gaussian random field is specified as in a linear model. It is ...
• Structural tests in additive regression AI翻译
  作者：Härdle, W; Sperlich, S; Spokoiny, V
  作者单位：Humboldt University of Berlin; Universidad Carlos III de Madrid
  摘要：We consider the component analysis problem for a regression model with an additive structure. The problem is to test whether some of the additive components are of polynomial structure (e.g., linear) without specifying the structure of the remaining components. A particular case is the problem of selecting the significant covariates. The method that we present is based on the wavelet transform using the Haar basis, which allows for applications under mild conditions on the design and smoothnes...
• Regularization of wavelet approximations - Discussion AI翻译
  作者：Moulin, P
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign
• Robust inference for generalized linear models AI翻译
  作者：Cantoni, E; Ronchetti, E
  作者单位：University of Geneva
  摘要：By starting from a natural class of robust estimators for generalized linear models based on the notion of qua-si-likelihood, we define robust deviances that can be used for stepwise model selection as in the classical framework. Wc derive the asymptotic distribution of tests based on robust deviances, and we investigate the stability of their asymptotic level under contamination. The binomial and Poisson models are treated in detail. Two applications to real data and a sensitivity analysis sh...
• Inference for density families using functional principal component analysis - Comment AI翻译
  作者：Ramsay, JO
  作者单位：McGill University
• A marginal model for analyzing discrete outcomes from longitudinal surveys with outcomes subject to multiple-cause nonresponse AI翻译
  作者：Miller, ME; Ten Have, TR; Reboussin, BA; Lohman, KK; Rejeski, WJ
  作者单位：Wake Forest University; Wake Forest Baptist Medical Center; University of Pennsylvania; Wake Forest University; Wake Forest Baptist Medical Center; Wake Forest University
  摘要：Techniques for analyzing categorical outcomes obtained from longitudinal survey samples, with outcomes subject to multiple-cause nonresponse, are developed within the framework, of weighted generalized estimating equations. Development of these techniques was motivated by disability data obtained from the Longitudinal Study of Aging (LSOA), a longitudinal survey sample containing missing follow-up for many elderly participants. We posit a model that combines different multivariate link functio...