An R package for model fitting, model selection and the simulation for longitudinal data with dropout missingness [0.03%]
一个用于具有脱落缺失的纵向数据分析建模、模型选择和模拟的R软件包
Cong Xu,Zheng Li,Yuan Xue et al.
Cong Xu et al.
Missing data arise frequently in clinical and epidemiological fields, in particular in longitudinal studies. This paper describes the core features of an R package wgeesel, which implements marginal model fitting (i.e., weighted generalized...
Monte Carlo studies of bootstrap variability in ROC analysis with data dependency [0.03%]
数据相关性对ROC分析中Bootstrap变化性影响的蒙特卡洛研究
Jin Chu Wu,Alvin F Martin,Raghu N Kacker
Jin Chu Wu
ROC analysis involving two large datasets is an important method for analyzing statistics of interest for decision making of a classifier in many disciplines. And data dependency due to multiple use of the same subjects exists ubiquitously ...
A study of the properties of Gaussian mixture model for stable isotope standard quantification in MALDI-TOF MS [0.03%]
高斯混合模型在基质辅助激光解析电离飞行时间质谱稳定同位素标准定量中的性质研究
John Christian G Spainhour,Michael G Janech,Viswanathan Ramakrishnan
John Christian G Spainhour
The quantification of peptides in Matrix assisted laser desorption/ionization time-of-flight mass spectrum analysis coupled with stable isotope standards has been used to quantify native peptides under many experimental conditions. This app...
Nezamoddin N Kachouie,Xihong Lin,Armin Schwartzman
Nezamoddin N Kachouie
Feature extraction from observed noisy samples is a common important problem in statistics and engineering. This paper presents a novel general statistical approach to the region detection problem in long data sequences. The proposed techni...
Nonparametric Bootstrap of Sample Means of Positive-Definite Matrices with an Application to Diffusion-Tensor-Imaging Data Analysis [0.03%]
扩散张量成像数据中正定矩阵样本均值的非参数自助法
Leif Ellingson,David Groisser,Daniel Osborne et al.
Leif Ellingson et al.
This paper presents nonparametric two-sample bootstrap tests formeans of randomsymmetric positivedefinite (SPD) matrices according to two differentmetrics: the Frobenius (or Euclidean)metric, inherited from the embedding of the set of SPD m...
Optimal inference for Simon's two-stage design with over or under enrollment at the second stage [0.03%]
Simon两阶段设计中第二阶段超量或减量抽样时的最优推断方法
Guogen Shan,John J Chen
Guogen Shan
Simon's two-stage designs are widely used in clinical trials to assess the activity of a new treatment. In practice, it is often the case that the second stage sample size is different from the planned one. For this reason, the critical val...
An efficient Bayesian approach for Gaussian Bayesian network structure learning [0.03%]
一种高效的高斯贝叶斯网络结构学习的贝叶斯方法
Shengtong Han,Hongmei Zhang,Ramin Homayouni et al.
Shengtong Han et al.
This article proposes a Bayesian computing algorithm to infer Gaussian directed acyclic graphs (DAG's). It has the ability of escaping local modes and maintaining adequate computing speed compared to existing methods. Simulations demonstrat...
An empirical approach to determine a threshold for assessing overdispersion in Poisson and negative binomial models for count data [0.03%]
一种确定阈值以评估计数数据的泊松和负二项模型过度离散的经验方法
Elizabeth H Payne,Mulugeta Gebregziabher,James W Hardin et al.
Elizabeth H Payne et al.
Overdispersion is a problem encountered in the analysis of count data that can lead to invalid inference if unaddressed. Decision about whether data are overdispersed is often reached by checking whether the ratio of the Pearson chi-square ...
An evaluation of the bootstrap for model validation in mixture models [0.03%]
Bootstrap法在混合模型中的评价及模型验证
Thomas Jaki,Ting-Li Su,Minjung Kim et al.
Thomas Jaki et al.
Bootstrapping has been used as a diagnostic tool for validating model results for a wide array of statistical models. Here we evaluate the use of the non-parametric bootstrap for model validation in mixture models. We show that the bootstra...
Bayesian Hierarchical Joint Modeling Using Skew-Normal/Independent Distributions [0.03%]
基于Skew-正态/独立分布的贝叶斯分层联合建模方法研究
Geng Chen,Sheng Luo
Geng Chen
The multiple longitudinal outcomes collected in many clinical trials are often analyzed by multilevel item response theory (MLIRT) models. The normality assumption for the continuous outcomes in the MLIRT models can be violated due to skewn...