487期

最新文章

• Reweighting Estimators for Cox Regression With Missing Covariates AI翻译
  作者：Xu, Qiang; Paik, Myunghee Cho; Luo, Xiaodong; Tsai, Wei-Yann
  作者单位：US Food & Drug Administration (FDA); Columbia University; Icahn School of Medicine at Mount Sinai
  摘要：Missingness in covariates is a common problem in survival data. In this article we propose a reweighting method for estimating the regression parameters in the Cox model with missing covariates. We also consider the augmented reweighting method by subtracting the projection term onto the nuisance tangent space. The proposed method provides consistent and asymptotically normally distributed estimators when the missing-data mechanism depends on the outcome variables, its well as on the observed ...
• Quantile Regression With Measurement Error AI翻译
  作者：Wei, Ying; Carroll, Raymond J.
  作者单位：Columbia University; Texas A&M University System; Texas A&M University College Station
  摘要：Regression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels. An iterative EM-type estimation algorithm to obtain the Solutions to Such joint estimation equations is provided. ...
• Nonparametric Prediction in Measurement Error Models Comment - Rejoinder AI翻译
  作者：Carroll, Raymond J.; Delaigle, Aurore; Hall, Peter
  作者单位：Texas A&M University System; Texas A&M University College Station; University of Bristol; University of Melbourne; University of California System; University of California Davis
• Thresholding Events of Extreme in Simultaneous Monitoring of Multiple Risks AI翻译
  作者：Einmahl, John H. J.; Li, Jun; Liu, Regina Y.
  作者单位：Tilburg University; Tilburg University; University of California System; University of California Riverside; Rutgers University System; Rutgers University New Brunswick
  摘要：This article develops a threshold system for monitoring airline performance. This threshold system divides the sample space into regions with increasing levels of risk and allows instant assessments of risk level of any observed airline performance. Of particular concern is the performance with extreme risk. In this article. a multivariate extreme value theory approach is used to establish thresholds for signaling varying levels of extremeness in the context of simultaneous monitoring of multi...
• 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...
• On Large Margin Hierarchical Classification With Multiple Paths AI翻译
  作者：Wang, Junhui; Shen, Xiaotong; Pan, Wei
  作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; University of Minnesota System; University of Minnesota Twin Cities; University of Minnesota System; University of Minnesota Twin Cities
  摘要：Hierarchical classification is critical to knowledge management and exploration. as is gene function prediction and document categorization. In hierarchical classification, an input is classified according to a structured hierarchy. In such a situation, the central issue is how to effectively utilize the interclass relationship to improve the generalization performance of flat classification ignoring such dependency. In this article, we propose a novel large margin method through constraints c...
• Predicting Vehicle Crashworthiness: Validation of Computer Models for Functional and Hierarchical Data AI翻译
  作者：Bayarri, M. J.; Berger, James O.; Kennedy, Marc C.; Kottas, Athanasios; Paulo, Rui; Sacks, Jerry; Cafeo, John A.; Lin, Chin-Hsu; Tu, Jian
  作者单位：University of Valencia; Duke University; Food & Environment Research Agency; University of California System; University of California Santa Cruz; Universidade de Lisboa; General Motors
  摘要：The CRASH computer model simulates the effect of a vehicle colliding against different barrier types. If it accurately represents real vehicle crashworthiness, the computer model can be of great value in various aspects of vehicle design, such as the setting of timing of air bag releases. The goal of this study is to address the problem of validating the computer model for such design goals, based on utilizing computer model runs and experimental data from real crashes. This task is complicate...
• Cox Models With Smooth Functional Effect of Covariates Measured With Error AI翻译
  作者：Cheng, Yu-Jen; Crainiceanu, Ciprian M.
  作者单位：Johns Hopkins University
  摘要：We propose, develop, and implement a fully Bayesian inferential approach for the Cox model when the log hazard function contains unknown smooth functions of the variables measured with error. Our approach is to model nonparametrically both the log-baseline hazard and the smooth components of the log-hazard functions using low-rank penalized splines. Careful implementation of the Bayesian inferential machinery is shown to produce remarkably better results than the naive approach. Our methodolog...
• Constructing Confidence Regions of Optimal Expected Size AI翻译
  作者：Schafer, Chad M.; Stark, Philip B.
  作者单位：Carnegie Mellon University; University of California System; University of California Berkeley
  摘要：This article presents a Monte Carlo method for approximating the minimax expected size (MES) confidence set for a parameter known to lie in a compact set. The algorithm is motivated by problems in the physical sciences in which parameters are unknown physical constants related to the distribution of observable phenomena through complex numerical models. The method repeatedly draws parameters at random from the parameter space and simulates data as if each of those values were the true value of...
• Weighted Normal Spatial Scan Statistic for Heterogeneous Population Data AI翻译
  作者：Huang, Lan; Tiwari, Ram C.; Zou, Zhaohui; Kulldorff, Martin; Feuer, Eric J.
  作者单位：National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH Division of Cancer Control & Population Sciences; US Food & Drug Administration (FDA); Information Management Services, Inc.; Harvard University; Harvard Medical School; Harvard Pilgrim Health Care
  摘要：In geographical spatial epidemiology and disease surveillance, all the existing spatial scan methods for cluster detection using continuous data are designed for evaluating clusters of individuals and analyzing individual-level data. Motivated by growing demands to study the spatial heterogeneity of continuous measures in population data, such as mortality rates, survival rates, average body mass indexes and pollution at state, county, and census tract levels. we propose a weighted normal scan...