Journal of the American Statistical Association

最新文章

• Robust and efficient adaptive estimation of binary-choice regression models AI翻译
  作者：Cizek, Pavel
  作者单位：Tilburg University
  摘要：The binary-choice regression models, such as probit and logit, are used to describe the effect of explanatory variables on a binary response variable. Typically estimated by the maximum likelihood method, estimates are very sensitive to deviations from a model, such as heteroscedasticity and data contamination. At the same time, the traditional robust (high-breakdown point) methods, such as the maximum trimmed likelihood, are not applicable because, by trimming observations, they induce nonide...
• An approach to multivariate covariate-dependent quantile contours with application to bivariate conditional growth charts AI翻译
  作者：Wei, Ying
  作者单位：Columbia University
  摘要：Multivariate quantile contours are useful in numerous applications and have been studied in different contexts. However, no easy solutions exist when dynamic and conditional quantile contours are needed without strong distributional assumptions. In this article we propose a new form of bivariate quantile contours and a two-stage estimation procedure to take time effect into account. The proposed procedure relies on quantile regression for longitudinal data and is flexible to include potentiall...
• Correlation-Based Functional Clustering via Subspace Projection AI翻译
  作者：Chiou, Jeng-Min; Li, Pai-Ling
  作者单位：Tamkang University
  摘要：A correlation-based functional clustering method is proposed for grouping curves with similar shapes. A correlation between two random functions defined through the functional inner product is used as a similarity measure. Curves with similar shapes are embedded in the cluster subspace spanned by a a mean shape function and eigenfunctions of the covariance kernel. The cluster membership prediction for each curve attempts to maximize the functional correlation between the observed and predicted...
• Domain-Level Covariance Analysis for Multilevel Survey Data With Structured Nonresponse AI翻译
  作者：O'Malley, A. James; Zaslavsky, Alan M.
  作者单位：Harvard University; Harvard Medical School
  摘要：Health care quality surveys in the United States are administered to individual respondents (i.e.. hospital patients, health plan members) to evaluate the performance of health care organizations (i.e., hospitals, health plans). which thus Constitute estimation domains. For better understanding and more parsimonious reporting of dimensions of quality, we analyze relationships among., quality Measures at the domain level. Rather than specifying a full parametric model for the observed responses...
• The Nested Dirichlet Process Comment AI翻译
  作者：Gillen, Daniel L.; Johnson, Wesley O.
  作者单位：University of California System; University of California Irvine
• The Nested Dirichlet Process Comment AI翻译
  作者：MacEachern, Steven N.
  作者单位：University System of Ohio; Ohio State University
• Election Forecasts Using Spatiotemporal Models AI翻译
  作者：Manuel Pavia, Jose; Larraz, Beatriz; Maria Montero, Jose
  作者单位：University of Valencia; Universidad de Castilla-La Mancha
  摘要：There exists a large literature on the problem of forecasting election results. But none of the published methods take spatial information into account, although there is clear evidence of geographic trends. To fill this gap, we use geostatical procedures to build a spatial model of voting patterns. We test the model in three close elections and find that it outperforms rival methods in current use. We apply kriging (a spatial model) and cokriging (in a spatiotemporal model version) to improve...
• A unified approach to nonparametric comparison of receiver operating characteristic curves for longitudinal and clustered data AI翻译
  作者：Li, Gang; Zhou, Kefei
  作者单位：University of California System; University of California Los Angeles; Perrigo Company PLC; Perrigo Company PLC North America
  摘要：We present a unified approach to nonparametric comparisons of receiver operating characteristic (ROC) curves for a paired design with clustered data. Treating empirical ROC curves as stochastic processes, their asymptotic joint distribution is derived in the presence of both between-marker and within-subject correlations. A Monte Carlo method is developed to approximate their joint distribution without involving nonparametric density estimation. The developed theory is applied to derive new in...
• Cure rate model with mismeasured covariates under transformation AI翻译
  作者：Ma, Yanyuan; Yin, Guosheng
  作者单位：University of Neuchatel; University of Texas System; UTMD Anderson Cancer Center
  摘要：Cure rate models explicitly account for the survival fraction in failure time data. When the covariates are measured with errors, naively treating mismeasured covariates as error-free would cause estimation bias and thus lead to incorrect inference. Under the proportional hazards cure model, we propose a corrected score approach as well as its generalization, and implement a transformation on the mismeasured covariates toward error additivity and/or normality. The corrected score equations can...
• Bayesian Inference on Changes in Response Densities Over Predictor Clusters AI翻译
  作者：Dunson, David B.; Herring, Amy H.; Siega-Riz, Anna Maria
  作者单位：Duke University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
  摘要：In epidemiology, it often is of interest to assess how individuals with different trajectories over time in an environmental exposure or biomarker differ with respect to a continuous response. For ease in interpretation and presentation of results. epidemiologists typically categorize predictors before analysis. To extend this approach to time-varying predictors, individuals can be clustered by then predictor trajectory, with the cluster index included as a predictor in a regression model for ...