454期

最新文章

• 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...
• 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...
• 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...
• Inference for density families using functional principal component analysis - Comment AI翻译
  作者：Ramsay, JO
  作者单位：McGill University
• Inference for density families using functional principal component analysis AI翻译
  作者：Kneip, A; Utikal, KJ
  作者单位：Johannes Gutenberg University of Mainz
  摘要：We consider t = 1,..., T samples of lid observations {X-1t,...,X-ntt} from unknown population densities {f(t)}. To characterize differences and similarities of {f(t)}, we assume their expansions into the first L principal components. From the given observations {X-it}, we study inference on the components and on their required number L. A detailed asymptotic theory is presented. Our method is applied in the analysis of yearly cross-sectional samples of British households. Interpretation of the...
• Vote tampering in a district justice election in Beaver County, PA AI翻译
  作者：DiMatteo, I; Kadane, JB
  作者单位：Carnegie Mellon University
  摘要：This article examines the evidence of vote tampering in a District Justice election in Beaver County, Pennsylvania. An informal exploratory data analysis and a legal history are followed by a formal Bayesian model of the data from the vote count on election night and the recount completed 2 months later. The evidence suggests that persons unknown could have gained access to the boxes containing the paper ballots. and surprising patterns of changes in the counts support the inference that certa...