Journal of the American Statistical Association

最新文章

• Simple robust testing of hypotheses in nonlinear models AI翻译
  作者：Bunzel, H; Kiefer, NM; Vogelsang, TJ
  作者单位：Iowa State University; Cornell University; Cornell University; Aarhus University
  摘要：We develop test statistics to test hypotheses in nonlinear weighted regression models with serial correlation or conditional heteroscedasticity of unknown form. The novel aspect is that these tests are simple and do not require the use of heteroseedasticity autocorrelation-consistent (HAC) covariance matrix estimators. Th-is new class of tests uses stochastic transformations to eliminate nuisance parameters as a substitute for consistently estimating the nuisance parameters. We derive the limi...
• Downweighting influential clusters in surveys: Application to the 1990 Post Enumeration Survey AI翻译
  作者：Zaslavsky, AM; Schenker, N; Belin, TR
  作者单位：Harvard University; Centers for Disease Control & Prevention - USA; CDC National Center for Health Statistics (NCHS); University of California System; University of California Los Angeles
  摘要：Certain clusters may be extremely influential on survey estimates and consequently contribute disproportionately to their variance, We propose a general approach to estimation that downweights highly influential clusters, with the amount of downweighting based on M-estimation applied to the empirical influence of the clusters, The method is motivated by a problem in census coverage estimation, and we illustrate it by using data from the 1990 Post Enumeration Survey (PES). in this context, an o...
• Goodness-of-fit tests for parametric regression models AI翻译
  作者：Fan, JQ; Huang, LS
  作者单位：Chinese University of Hong Kong; University of North Carolina; University of North Carolina Chapel Hill; University of Rochester
  摘要：Several new tests are proposed for examining the adequacy of a family of parametric models against large nonparametric alternatives. These tests formally check if the bias vector of residuals from parametric fts is negligible by using the adaptive Neyman test and other methods. The testing procedures formalize the traditional model diagnostic tools based on residual plots. We examine the rates of contiguous alternatives that can be detected consistently by the adaptive Neyman test. Application...
• Regularization of wavelet approximations - Discussion AI翻译
  作者：Cai, TT
  作者单位：University of Pennsylvania
• 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...