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• The estimation of prediction error: Covariance penalties and cross-validation - Comment AI翻译
  作者：Burman, P
  作者单位：University of California System; University of California Davis
• The estimation of prediction error: Covariance penalties and cross-validation AI翻译
  作者：Efron, B
  作者单位：Stanford University
  摘要：Having constructed a data-based estimation rule, perhaps a logistic regression or a classification tree, the statistician would like to know its performance as a predictor of future cases. There are two main theories concerning prediction error: (I) penalty methods such as C-p, Akaike's information criterion, and Stein's unbiased risk estimate that depend on the covariance between data points and their corresponding predictions; and (2) cross-validation and related nonparametric bootstrap tech...
• Getting it right: Joint distribution tests of posterior simulators AI翻译
  作者：Geweke, J
  作者单位：University of Iowa; University of Iowa
  摘要：Analytical or coding errors in posterior simulators can produce reasonable but incorrect approxii nations of posterior moments. This article develops simple tests of posterior simulators that detect both kinds of errors, and uses them to detect and correct errors in two previously published articles. The tests exploit the fact that a Bayesian model specifies the joint distribution of observables (data) and unobservables (parameters). There are two joint distribution simulators. The roarginal-c...
• The estimation of prediction error: Covariance penalties and cross-validation - Comment AI翻译
  作者：Shen, XT; Huang, HC; Ye, JM
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; Academia Sinica - Taiwan; City University of New York (CUNY) System; Baruch College (CUNY)
• Stable and efficient multiple smoothing parameter estimation for generalized additive models AI翻译
  作者：Wood, SN
  作者单位：University of Glasgow
  摘要：Representation of generalized additive models (GAM's) using penalized regression splines allows GAM's to be employed in a straightforward manner using penalized regression methods. Not only is inference facilitated by this approach, but it is also possible to integrate model selection in the form of smoothing parameter selection into model fitting in a computationally efficient manner using well founded criteria such as generalized cross-validation. The current fitting and smoothing parameter ...
• Optimal Bayesian design by inhomogeneous Markov chain simulation AI翻译
  作者：Müller, P; Sansó, B; De Iorio, M
  作者单位：University of Texas System; UTMD Anderson Cancer Center; University of California System; University of California Santa Cruz; Simon Bolivar University; Imperial College London
  摘要：We consider decision problems defined by a utility function and an underlying probability model for all unknowns. The utility function quantifies the decision maker's preferences over consequences. The optimal decision maximizes the expected utility function where the expectation is taken with respect to all unknowns, that is, future data and parameters. In many problems, the solution is not analytically tractable. For example, the utility function might involve moments that can be computed on...
• The estimation of prediction error: Covariance penalties and cross-validation - Comment AI翻译
  作者：Zhang, CM
  作者单位：University of Wisconsin System; University of Wisconsin Madison
• Membership functions and probability measures of fuzzy sets - Comment AI翻译
  作者：Laviolette, M
• Calibrated probabilistic mesoscale weather field forecasting: The geostatistical output perturbation method - Comment AI翻译
  作者：Roulston, MS
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
• Variable selection and model building via likelihood basis pursuit AI翻译
  作者：Zhang, HH; Wahba, G; Lin, Y; Voelker, M; Ferris, M; Klein, R; Klein, B
  作者单位：North Carolina State University; University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison
  摘要：This article presents a nonparametric penalized likelihood approach for variable selection and model building, called likelihood basis pursuit (LBP). In the setting of a tenser product reproducing kernel Hilbert space, we decompose the log-likelihood into the sum of different functional components such as main effects and interactions, with each component represented by appropriate basis functions. Basis functions are chosen to be compatible with variable selection and model building in the co...