基本信息来源于合作网站,原文需代理用户跳转至来源网站获取       
摘要:
In this paper we reparameterize covariance structures in longitudinal data analysis through the modified Cholesky decomposition of itself. Based on this modified Cholesky decomposition, the within-subject covariance matrix is decomposed into a unit lower triangular matrix involving moving average coefficients and a diagonal matrix involving innovation variances, which are modeled as linear functions of covariates. Then, we propose a penalized maximum likelihood method for variable selection in joint mean and covariance models based on this decomposition. Under certain regularity conditions, we establish the consistency and asymptotic normality of the penalized maximum likelihood estimators of parameters in the models. Simulation studies are undertaken to assess the finite sample performance of the proposed variable selection procedure.
推荐文章
Statistics matters in interpretations of non-traditional stable isotopic data
Isotopic data processing
Error propagation
Significant digits
Difference between means with uncertainties
Zircon saturation model in silicate melts: a review and update
Zircon
Zircon saturation
Model
Silicate melt
Mafic to silicic melts
Peraluminous to peralkaline compositions
Igneous rocks
Thermometer
内容分析
关键词云
关键词热度
相关文献总数  
(/次)
(/年)
文献信息
篇名 Joint Variable Selection of Mean-Covariance Model for Longitudinal Data
来源期刊 统计学期刊(英文) 学科 数学
关键词 JOINT Mean and COVARIANCE Models Variable Selection Cholesky DECOMPOSITION Longitudinal Data Penalized MAXIMUM LIKELIHOOD Method
年,卷(期) 2013,(1) 所属期刊栏目
研究方向 页码范围 27-35
页数 9页 分类号 O1
字数 语种
DOI
五维指标
传播情况
(/次)
(/年)
引文网络
引文网络
二级参考文献  (0)
共引文献  (0)
参考文献  (0)
节点文献
引证文献  (0)
同被引文献  (0)
二级引证文献  (0)
2013(0)
  • 参考文献(0)
  • 二级参考文献(0)
  • 引证文献(0)
  • 二级引证文献(0)
研究主题发展历程
节点文献
JOINT
Mean
and
COVARIANCE
Models
Variable
Selection
Cholesky
DECOMPOSITION
Longitudinal
Data
Penalized
MAXIMUM
LIKELIHOOD
Method
研究起点
研究来源
研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
统计学期刊(英文)
半月刊
2161-718X
武汉市江夏区汤逊湖北路38号光谷总部空间
出版文献量(篇)
584
总下载数(次)
0
总被引数(次)
0
论文1v1指导