457期

最新文章

• Adaptive model selection AI翻译
  作者：Shen, XT; Ye, JM
  作者单位：University System of Ohio; Ohio State University; City University of New York (CUNY) System; Baruch College (CUNY)
  摘要：Most model selection procedures use a fixed penalty penalizing an increase in the size of a model. These nonadaptive selection procedures perform well only in one type of situation. For instance, Bayesian information criterion (BIC) with a large penalty per-forms well for small models and poorly for large models, and Akaike's information criterion (AIC) does just the opposite. This article proposes an adaptive model selection procedure that uses a data-adaptive complexity penalty based on a co...
• Bayesian smoothing and regression splines for measurement error problems AI翻译
  作者：Berry, SM; Carroll, RJ; Ruppert, D
  作者单位：Berry Consultants, LLC; Texas A&M University System; Texas A&M University College Station; Cornell University
  摘要：In the presence of covariate measurement error, estimating a regression function nonparametrically is extremely difficult, the problem being related to deconvolution. Various frequentist approaches exist for this problem, but to date there has been no Bayesian treatment. In this article we describe Bayesian approaches to modeling a flexible regression function when the predictor variable is measured with error. The regression function is modeled with smoothing splines and regression P-splines....
• Recursive combination tests AI翻译
  作者：Brannath, W; Posch, M; Bauer, P
  作者单位：University of Vienna
  摘要：We present a method that extends the flexibility of adaptive designs to the number of interim analyses and to the choice of decision boundaries. At each stage of the trial, the design of the next stage can be determined using all of the information gathered so far. Additionally, one can specify the next stage as the final stage or can plan a further interim analysis. The method is based on a recursive application of the two-stage combination tests for p values. The crucial point is the appropr...
• Statistical methods in assessing agreement: Models, issues, and tools AI翻译
  作者：Lin, L; Hedayat, AS; Sinha, B; Yang, M
  作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Indian Statistical Institute; Indian Statistical Institute Kolkata
  摘要：Measurements of agreement are needed to assess the acceptability of a new or generic process, methodology, and formulation in areas of laboratory performance, instrument or assay validation, method comparisons, statistical process control, goodness of fit, and individual bioequivalence, In all of these areas, one needs measurements that capture a large proportion of data that are within a meaningful boundary from target values. Target values can be considered random (measured with error) or fi...
• Inconsistency of resampling algorithms for high-breakdown regression estimators and a new algorithm - Comment AI翻译
  作者：Maronna, RA; Yohai, VJ
  作者单位：National University of La Plata; University of Buenos Aires; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET)
• Latent class models for joint analysis of longitudinal biomarker and event process data: Application to longitudinal prostate-specific antigen readings and prostate cancer AI翻译
  作者：Lin, HQ; Turnbull, BW; McCulloch, CE; Slate, EH
  作者单位：Yale University; Cornell University; University of California System; University of California San Francisco; Medical University of South Carolina
  摘要：A retrospective substudy of the nutritional prevention of cancer (NPC) trials investigated the utility of longitudinally measured prostate-specific antigen (PSA) as a biomarker for subsequent onset of prostate cancer (PCa). Serial PSA levels were determined retrospectively from frozen blood samples that had been collected from all patients at successive clinic visits with the timing and the number of these visits highly variable. Diagnosis dates of all incident cases of PCa were recorded. Hete...