Journal of the American Statistical Association

最新文章

• Statistical analysis of diffusion tensors in diffusion-weighted magnetic resonance imaging data - Comment AI翻译
  作者：Lazar, Nicole A.
  作者单位：University System of Georgia; University of Georgia
• Strictly proper scoring rules, prediction, and estimation AI翻译
  作者：Gneiting, Tilmann; Raftery, Adrian E.
  作者单位：University of Washington; University of Washington Seattle
  摘要：Scoring rules assess the quality of probabilistic forecasts, by assigning a numerical score based on the predictive distribution and on the event or value that materializes. A scoring rule is proper. if the forecaster maximizes the expected score for an observation drawn from the distribution F if he or she issues the probabilistic forecast F, rather than G 4 F. It is strictly proper if the maximum is unique. In prediction problems, proper scoring rules encourage the forecaster to make careful...
• Power transformation toward a linear regression quantile AI翻译
  作者：Mu, Yunming; He, Xuming
  作者单位：Texas A&M University System; Texas A&M University College Station; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：In this article we consider the linear quantile regression model with a power transformation on the dependent variable. Like the classical Box-Cox transformation approach, it extends the applicability of linear models without resorting to nonparametric smoothing, but transformations on the quantile models are more natural due to the equivariance property of the quantiles under monotone transformations. We propose an estimation procedure and establish its consistency and asymptotic normality un...
• Sequential implementation of stepwise procedures for identifying the maximum tolerated dose AI翻译
  作者：Cheung, Ying Kuen
  作者单位：Columbia University
  摘要：This article considers the problem of finding the maximum tolerated dose (MTD) of a drug in human trials. The MTD is defined as the maximum test dose with toxicity probability less than or equal to a target toxicity rate. We adopt the multiple test framework, with step-down tests used in an escalation stage and step-up tests used in a deescalation stage, to allow sequential dose assignments for ethical purposes. By formulating the estimation problem as a testing problem, the proposed procedure...
• Untitled AI翻译
  作者：Ibrahim, Joseph G.; Zhu, Hongtu
  作者单位：University of North Carolina; University of North Carolina Chapel Hill
• Robust modeling for inference from generalized linear model classes AI翻译
  作者：Noh, Maengseok; Lee, Youngjo
  作者单位：Pukyong National University; Seoul National University (SNU)
  摘要：Generalized linear models (GLMs) are widely used for data analysis; however, their maximum likelihood estimators can be sensitive to outliers. We propose new statistical models that allow robust inferences from the GLM class of models, including Poisson and binomial GLMs, and their extension to generalized linear mixed models. The likelihood score equations from the new models give estimators with bounded influence, so that the resulting estimators are robust against outliers while maintaining...
• Partially linear hazard regression for multivariate survival data AI翻译
  作者：Cai, Jianwen; Fan, Jianqing; Jiang, Jiancheng; Zhou, Haibo
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; Princeton University; University of North Carolina; University of North Carolina Charlotte
  摘要：This article studies estimation of partially linear hazard regression models for multivariate survival data. A profile pseudo-partial likelihood estimation method is proposed under the marginal hazard model framework. The estimation on the parameters for the linear part is accomplished by maximization of a pseudo-partial likelihood profiled over the nonparametric part. This enables us to obtain root n-consistent estimators of the parametric component. Asymptotic normality is obtained for the e...
• A sturdy reduced-bias extreme quantile (VaR) estimator AI翻译
  作者：Gomes, M. Ivette; Pestana, Dinis
  作者单位：Universidade de Lisboa
  摘要：The main objective of statistics of extremes lies in the estimation of quantities related to extreme events. In many areas of application, such as statistical quality control, insurance, and finance, a typical requirement is to estimate a high quantile, that is, the value at risk at a level p (VaR(p)), high enough so that the chance of an exceedance of that value is equal to p, small. In this article we deal with the semiparametric estimation of VaRp for heavy tails. The classical semiparametr...
• Quantile regression in reproducing kernel Hilbert spaces AI翻译
  作者：Li, Youjuan; Liu, Yufeng; Zhu, Ji
  作者单位：University of Michigan System; University of Michigan; University of North Carolina; University of North Carolina Chapel Hill
  摘要：In this article we consider quantile regression in reproducing kernel Hilbert spaces, which we call kernel quantile regression (KQR). We make three contributions: (1) we propose an efficient algorithm that computes the entire solution path of the KQR, with essentially the same computational cost as fitting one KQR model; (2) we derive a simple formula for the effective dimension of the KQR model, which allows convenient selection of the regularization parameter; and (3) we develop an asymptoti...
• Stochastic approximation in Monte Carlo computation AI翻译
  作者：Liang, Faming; Liu, Chuanhai; Carroll, Raymond J.
  作者单位：Texas A&M University System; Texas A&M University College Station; Purdue University System; Purdue University
  摘要：The Wang-Landau (WL) algorithm is an adaptive Markov chain Monte Carlo algorithm used to calculate the spectral density for a physical system. A remarkable feature of the WL algorithm is that it is not trapped by local energy minima, which is very important for systems with rugged energy landscapes. This feature has led to many successful applications of the algorithm in statistical physics and biophysics; however, there does not exist rigorous theory to support its convergence, and the estima...