Temporal Prediction of Future State Occupation in a Multistate Model from High-Dimensional Baseline Covariates via Pseudo-Value Regression [0.03%]
基于伪值回归的高维基线协变量在多状态模型中对未来状态占据的时间预测
Sandipan Dutta,Susmita Datta,Somnath Datta
Sandipan Dutta
In many complex diseases such as cancer, a patient undergoes various disease stages before reaching a terminal state (say disease free or death). This fits a multistate model framework where a prognosis may be equivalent to predicting the s...
Bayesian Computation for Log-Gaussian Cox Processes: A Comparative Analysis of Methods [0.03%]
基于Log-高斯Cox过程的贝叶斯计算方法比较分析
Ming Teng,Farouk S Nathoo,Timothy D Johnson
Ming Teng
The Log-Gaussian Cox Process is a commonly used model for the analysis of spatial point pattern data. Fitting this model is difficult because of its doubly-stochastic property, i.e., it is an hierarchical combination of a Poisson process at...
Fast Bayesian Variable Screenings for Binary Response Regressions with Small Sample Size [0.03%]
小样本下二值响应回归的快速贝叶斯变量筛选方法
S-M Chang,J-Y Tzeng,R-B Chen
S-M Chang
Screening procedures play an important role in data analysis, especially in high-throughput biological studies where the datasets consist of more covariates than independent subjects. In this article, a Bayesian screening procedure is intro...
Calibrating the prior distribution for a normal model with conjugate prior [0.03%]
共轭先验下的正态模型的先验分布校准
Susan A Alber,J Jack Lee
Susan A Alber
For a normal model with a conjugate prior, we provide an in depth examination of the effects of the hyperparameters on the long-run frequentist properties of posterior point and interval estimates. Under an assumed sampling model for the da...
Estimation of the linear mixed integrated Ornstein-Uhlenbeck model [0.03%]
线性混合积分奥尔斯特恩-乌伦贝克模型的估计问题研究
Rachael A Hughes,Michael G Kenward,Jonathan A C Sterne et al.
Rachael A Hughes et al.
The linear mixed model with an added integrated Ornstein-Uhlenbeck (IOU) process (linear mixed IOU model) allows for serial correlation and estimation of the degree of derivative tracking. It is rarely used, partly due to the lack of availa...
Lili Yang,Menggang Yu,Sujuan Gao
Lili Yang
Joint models are statistical tools for estimating the association between time-to-event and longitudinal outcomes. One challenge to the application of joint models is its computational complexity. Common estimation methods for joint models ...
Concurrent generation of multivariate mixed data with variables of dissimilar types [0.03%]
一类不同于其他类的多元混合数据的并发生成
Anup Amatya,Hakan Demirtas
Anup Amatya
Data sets originating from wide range of research studies are composed of multiple variables that are correlated and of dissimilar types, primarily of count, binary/ordinal and continuous attributes. The present paper builds on the previous...
A covariance correction that accounts for correlation estimation to improve finite-sample inference with generalized estimating equations: A study on its applicability with structured correlation matrices [0.03%]
一种协方差校正方法，该方法考虑了相关性估计以改善广义估计方程的有限样本推理：对其与结构化相关矩阵结合使用的适用性的研究
Philip M Westgate
Philip M Westgate
When generalized estimating equations (GEE) incorporate an unstructured working correlation matrix, the variances of regression parameter estimates can inflate due to the estimation of the correlation parameters. In previous work, an approx...
A modified ziggurat algorithm for generating exponentially- and normally-distributed pseudorandom numbers [0.03%]
一种生成指数和正态分布伪随机数的改进阶梯算法
Christopher D McFarland
Christopher D McFarland
The Ziggurat Algorithm is a very fast rejection sampling method for generating PseudoRandom Numbers (PRNs) from statistical distributions. In the algorithm, rectangular sampling domains are layered on top of each other (resembling a ziggura...
Variable selection models based on multiple imputation with an application for predicting median effective dose and maximum effect [0.03%]
基于多重插补的变量选择模型及其在预测中效剂量和最大效应中的应用
Y Wan,S Datta,D J Conklin et al.
Y Wan et al.
The statistical methods for variable selection and prediction could be challenging when missing covariates exist. Although multiple imputation (MI) is a universally accepted technique for solving missing data problem, how to combine the MI ...