Journal of the American Statistical Association

最新文章

• Dose-finding based on multiple toxicities in a soft tissue sarcoma trial AI翻译
  作者：Bekele, BN; Thall, PF
  作者单位：University of Texas System; UTMD Anderson Cancer Center
  摘要：The scientific goal of a phase I oncology trial of a new chemotherapeutic agent is to find a dose with an acceptable level of toxicity. For ethical reasons, dose-finding is done adaptively, with doses chosen for successive cohorts of patients based on the data obtained from previous cohorts. Typically. patients are at risk for several qualitatively different toxicities, each occurring at several possible severity levels. In this article, we describe how we addressed the dose-finding problem in...
• Bayesian estimation of the spectral density of a time series AI翻译
  作者：Choudhuri, N; Ghosal, S; Roy, A
  作者单位：University System of Ohio; Case Western Reserve University; North Carolina State University; University System of Maryland; University of Maryland Baltimore County
  摘要：This article describes a Bayesian approach to estimating the spectral density of a stationary time series. A nonparametric prior on the spectral density is described through Bernstein polynomials. Because the actual likelihood is very complicated, a pseudoposterior distribution is obtained by updating the prior using the Whittle likelihood. A Markov chain Monte Carlo algorithm for sampling front this posterior distribution is described that is used for computing the posterior mean, variance, a...
• Smoothing spline nonlinear nonparametric regression models AI翻译
  作者：Ke, CL; Wang, YD
  作者单位：University of California System; University of California Santa Barbara
  摘要：Almost all of the current nonparametric regression methods, such as smoothing splines, generalized additive models, and varying-coefficients models, assume a linear relationship when nonparametric functions are regarded as parameters. In this article we propose a general class of smoothing spline nonlinear nonparametric models that allow nonparametric functions to act nonlinearly. They arise in many fields as either theoretical or empirical models. Our new estimation methods are based on an ex...
• Local global neural networks:: A new approach for nonlinear time series modeling AI翻译
  作者：Suárez-Fariñas, M; Pedreira, CE; Medeiros, MC
  作者单位：Pontificia Universidade Catolica do Rio de Janeiro; Pontificia Universidade Catolica do Rio de Janeiro
  摘要：We propose the local-global neural networks model within the context of time series models. This formulation encompasses some already existing nonlinear models and also admits the mixture of experts approach. We emphasize the linear expert case and extensively discuss the theoretical aspects of the model: stationarity conditions, existence, consistency and asymptotic normality of the parameter estimates, and model identifiability. The proposed model consists of a mixture of stationary and nons...
• Calibrated probabilistic mesoscale weather field forecasting: The geostatistical output perturbation method - Comment AI翻译
  作者：Tebaldi, C; Nychka, D
  作者单位：National Center Atmospheric Research (NCAR) - USA
• Location-scale depth - Comment AI翻译
  作者：He, XM; Portnoy, S
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign
• Smooth design-adapted wavelets for nonparametric stochastic regression AI翻译
  作者：Delouille, V; Simoens, J; von Sachs, R
  作者单位：Rice University; Universite Catholique Louvain; Universite Catholique Louvain
  摘要：We treat nonparametric stochastic regression using smooth design-adapted wavelets built by means of the lifting scheme. The proposed method automatically adapts to the nature of the regression problem, that is, to the irregularity of the design, to data on the interval, and to arbitrary sample sizes (which do not need to be a power of 2). As such, this method provides a uniform solution to the usual criticisms of first-generation wavelet estimators. More precisely, starting from the unbalanced...
• Hierarchical Bayesian neural networks: An application to a prostate cancer study AI翻译
  作者：Ghosh, M; Maiti, T; Kim, D; Chakraborty, S; Tewari, A
  作者单位：State University System of Florida; University of Florida; Iowa State University; Kyungpook National University (KNU); Henry Ford Health System; Henry Ford Hospital; Henry Ford Health System; Henry Ford Hospital; Henry Ford Health System; Henry Ford Hospital
  摘要：Prostate cancer is one of the most common cancers in American men. Management depends on the staging of prostate cancer. Only cancers that are confined to organs of origin are potentially curable. The article considers a hierarchical Bayesian neural network approach for posterior prediction probabilities of certain features indicative of non-organ-confined prostate cancer. The Bayesian procedure is implemented by an application of the Markov chain Monte Carlo numerical integration technique. F...
• A Bayesian insertion/deletion algorithm for distant protein motif searching via entropy filtering AI翻译
  作者：Xie, J; Li, KC; Bina, M
  作者单位：Purdue University System; Purdue University; University of California System; University of California Los Angeles; Purdue University System; Purdue University
  摘要：Bayesian models have been developed that find ungapped motifs in multiple protein sequences. In (his article. we extend the model to allow for deletions and insertions in motifs. Direct generalization of the ungapped algorithm, based on Gibbs sampling, proved unsuccessful because the configuration space became much larger. To alleviate the convergence difficulty, a two-stage procedure is introduced. At the first stage. we develop a method called entropy filtering, which quick]), searchs good s...
• Full matching in an observational study of coaching for the SAT AI翻译
  作者：Hansen, BB
  作者单位：University of Michigan System; University of Michigan
  摘要：Among matching techniques for observational studies, full matching is in principle the best, in the sense that its alignment of comparable treated and control subjects is as good as that of any alternate method, and potentially much better. This article evaluates the practical performance of full matching for the first time, modifying it in order to minimize variance as well as bias and then using it to compare coached and uncoached takers of the SAT. In this new version, with restrictions on ...