Journal of the American Statistical Association

最新文章

• Median-Based Classifiers for High-Dimensional Data AI翻译
  作者：Hall, Peter; Titterington, D. M.; Xue, Jing-Hao
  作者单位：University of Melbourne; University of Glasgow; University of London; University College London
  摘要：Conventional distance-based classifiers use standard Euclidean distance, and so can suffer from excessive volatility if vector components have heavy-tailed distributions. This difficulty can be alleviated by replacing the L-2 distance by its L-1 counterpart. For example, the L-1 version of the popular centroid classifier would allocate a new data value to the population to whose centroid it was closest in L-1 terms. However, this approach can lead to inconsistency, because the centroid is defi...
• Locally Weighted Censored Quantile Regression AI翻译
  作者：Wang, Huixia Judy; Wang, Lan
  作者单位：North Carolina State University; University of Minnesota System; University of Minnesota Twin Cities
  摘要：Censored quantile regression offers a valuable supplement to Cox proportional hazards model for survival analysis. Existing work in the literature often requires stringent assumptions, such as unconditional independence of the survival time and the censoring variable or global linearity at all quantile levels. Moreover, some of the work uses recursive algorithms. making it challenging to derive asymptotic normality. To overcome these drawbacks, we propose a new locally weighted censored quanti...
• Assessing Sexual Attitudes and Behaviors of Young Women: A Joint Model with Nonlinear Time Effects, Time Varying Covariates, and Dropouts AI翻译
  作者：Ghosh, Pulak; Tu, Wanzhu
  作者单位：University System of Georgia; Georgia State University; Indiana University System; Indiana University Indianapolis; Regenstrief Institute Inc
  摘要：Understanding human sexual behaviors is essential for the effective prevention of sexually transmitted infections (STI). Analysis of longitudinally measured sexual behavioral data, however, is often complicated by zero-inflation of event counts, nonlinear time trend, time-varying covariates, and informative dropouts. Ignoring these complicating factors could undermine the validity of the study findings. In this article, we put forth a unified joint modeling structure that accommodates these fe...
• On the Concept of Depth for Functional Data AI翻译
  作者：Lopez-Pintado, Sara; Romo, Juan
  作者单位：Universidad Pablo de Olavide; Universidad Carlos III de Madrid
  摘要：The statistical analysis of functional data is a growing need in many research areas. In particular, a robust methodology is important to study curves, which are the output of many experiments in applied statistics. As a starting point for this robust analysis. we propose, analyze. and apply a new definition of depth for functional based on the graphic observations based presentation of the curves. Given a collection of functions. it establishes the centrality of an observation and provides a ...
• Robust Estimation of Mean Functions and Treatment Effects for Recurrent Events Under Event-Dependent Censoring and Termination: Application to Skeletal Complications in Cancer Metastatic to Bone AI翻译
  作者：Cook, Richard J.; Lawless, Jerald F.; Lakhal-Chaieb, Lajmi; Lee, Ker-Ai
  作者单位：University of Waterloo; Laval University
  摘要：In clinical trials featuring recurrent clinical events, the definition and estimation of treatment effects involves a number of interesting issues, especially when loss to follow-up may be event-related and when terminal events such as death preclude the occurrence of further events. This paper discusses a clinical trial of breast cancer patients with bone metastases where the recurrent events are skeletal complications, and where patients may die during the trial. We argue that treatment effe...
• A Spatio-Temporal Model for Mean, Anomaly, and Trend Fields of North Atlantic Sea Surface Temperature Comment AI翻译
  作者：Mendelssohn, Roy
  作者单位：National Oceanic Atmospheric Admin (NOAA) - USA
• A Bayesian Model for Cross-Study Differential Gene Expression Comment AI翻译
  作者：Ghosh, Debashis; Choi, Hyungwon
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
• What Are the Limits of Posterior Distributions Arising From Nonidentified Models, and Why Should We Care? AI翻译
  作者：Gustafson, Paul
  作者单位：University of British Columbia
  摘要：In health research and other fields. the observational data available to researchers often fall short of the data that ideally would be available, due to the inherent limitations of study design and data acquisition. Were they available, these ideal data might be readily analyzed via straightforward statistical models with such desirable properties as parameter identifiability. Conversely, realistic models for the available data that incorporate uncertainty about the link between ideal and ava...
• Local Rank Inference for Varying Coefficient Models AI翻译
  作者：Wang, Lan; Kai, Bo; Li, Runze
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; College of Charleston; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：By allowing the regression coefficients to change with certain covariates, the class of varying coefficient models offers a flexible approach to modeling nonlinearity and interactions between covariates. This article proposes a novel estimation procedure for the varying coefficient models based on local ranks. The new procedure provides a highly efficient and robust alternative to the local linear least squares method. and can be conveniently implemented using existing R software package. Theo...
• Conditional Quantile Estimation for Generalized Autoregressive Conditional Heteroscedasticity Models AI翻译
  作者：Xiao, Zhijie; Koenker, Roger
  作者单位：Boston College; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Conditional quantile estimation is an essential ingredient in modem risk management. Although generalized autoregressive conditional heteroscedasticity (GARCH) processes have proven highly successful in modeling financial data, it is generally recognized that it would be useful to consider a broader class of processes capable of representing more flexibly both asymmetry and tail behavior of conditional returns distributions. In this article we study estimation of conditional quantiles for GARC...