The empirical likelihood prior applied to bias reduction of general estimating equations [0.03%]
经验似然先验在泛估计方程偏差校正中的应用
Albert Vexler,Li Zou,Alan D Hutson
Albert Vexler
The practice of employing empirical likelihood (EL) components in place of parametric likelihood functions in the construction of Bayesian-type procedures has been well-addressed in the modern statistical literature. The EL prior, a Jeffrey...
A Gamma-frailty proportional hazards model for bivariate interval-censored data [0.03%]
带伽玛 frailty 的比例危险模型在双变量区间删失数据中的应用
Prabhashi W Withana Gamage,Christopher S McMahan,Lianming Wang et al.
Prabhashi W Withana Gamage et al.
Correlated survival data naturally arise from many clinical and epidemiological studies. For the analysis of such data, the Gamma-frailty proportional hazards (PH) model is a popular choice because the regression parameters have marginal in...
Covariance-Insured Screening [0.03%]
协方差保底筛选法
Kevin He,Jian Kang,Hyokyoung G Hong et al.
Kevin He et al.
Modern bio-technologies have produced a vast amount of high-throughput data with the number of predictors far greater than the sample size. In order to identify more novel biomarkers and understand biological mechanisms, it is vital to dete...
Sy Han Chiou,Jing Qian,Elizabeth Mormino et al.
Sy Han Chiou et al.
Truncated survival data arise when the event time is observed only if it falls within a subject-specific region, known as the truncation set. Left-truncated data arise when there is delayed entry into a study, such that subjects are include...
Bayesian Functional Joint Models for Multivariate Longitudinal and Time-to-Event Data [0.03%]
多元纵向和生存数据的贝叶斯函数联合模型研究
Kan Li,Sheng Luo
Kan Li
A multivariate functional joint model framework is proposed which enables the repeatedly measured functional outcomes, scalar outcomes, and survival process to be modeled simultaneously while accounting for association among the multiple (f...
Jackknife empirical likelihood method for multiply robust estimation with missing data [0.03%]
多稳健估计的刀切经验似然方法及其缺失数据推断
Sixia Chen,David Haziza
Sixia Chen
A novel jackknife empirical likelihood method for constructing confidence intervals for multiply robust estimators is proposed in the context of missing data. Under mild regularity conditions, the proposed jackknife empirical likelihood rat...
A Forward and Backward Stagewise Algorithm for Nonconvex Loss Functions with Adaptive Lasso [0.03%]
具有自适应Lasso的非凸损失函数的前进和后退分步算法
Xingjie Shi,Yuan Huang,Jian Huang et al.
Xingjie Shi et al.
Penalization is a popular tool for multi- and high-dimensional data. Most of the existing computational algorithms have been developed for convex loss functions. Nonconvex loss functions can sometimes generate more robust results and have i...
Measuring Model Misspecification: Application to Propensity Score Methods with Complex Survey Data [0.03%]
度量模型指定错误：在复杂调查数据中的倾向值方法的应用
David Lenis,Benjamin Ackerman,Elizabeth A Stuart
David Lenis
Model misspecification is a potential problem for any parametric-model based analysis. However, the measurement and consequences of model misspecification have not been well formalized in the context of causal inference. A measure of model ...
Dewei Wang,Christopher S McMahan,Joshua M Tebbs et al.
Dewei Wang et al.
Screening procedures for infectious diseases, such as HIV, often involve pooling individual specimens together and testing the pools. For diseases with low prevalence, group testing (or pooled testing) can be used to classify individuals as...
Analysis of Generalized Semiparametric Regression Models for Cumulative Incidence Functions with Missing Covariates [0.03%]
具有缺失协变量的累积发病率函数的广义半参数回归模型分析
Unkyung Lee,Yanqing Sun,Thomas H Scheike et al.
Unkyung Lee et al.
The cumulative incidence function quantifies the probability of failure over time due to a specific cause for competing risks data. The generalized semiparametric regression models for the cumulative incidence functions with missing covaria...