Leave-subject-out Model Averaging and Its Application to High Dimensional Data

来源 :泛华统计学会(icsa)2015年学术会议 | 被引量 : 0次 | 上传用户:jieshoukode
下载到本地 , 更方便阅读
声明 : 本文档内容版权归属内容提供方 , 如果您对本文有版权争议 , 可与客服联系进行内容授权或下架
论文部分内容阅读
This paper develops a frequentist model averaging method based on the leave-subject-out cross-validation.This method is applicable not only to averaging longitudinal data models,but also to averaging time series models which can have serially dependent errors.
其他文献
Gene expression Quantitative trait Loci(eQTLs) are genetic loci that regulate gene expression,and eQTL mapping is the process to identify such genetic loci.
会议
This paper develops a new model averaging approach based on an approximate generalized crossvalidation which can be applied to both threshold and general linear
会议
A myriad of model selection strategies have been employed in statistics to determine a model for data analysis,and further study and inference proceed as though
会议
Genetical genomics data provide promising opportunities for integrative analysis of gene expression and genotype data.Lin et al.(2015) recently proposed an inst
会议
Akaike Information Criterion,which is based on maximum likelihood estimation and cannot be applied directly to the situations when likelihood functions are not
会议
We discuss the use of the determinantal point process(DPP) as a prior for latent structure in biomedical models when the goal is to interpret latent features as
会议
Model selection methods are proposed for different models.Some are restricted to specific models and others require strict conditions which are probably hard to
会议
Markov chain Monte Carlo(MCMC) methods have proven to be a very powerful tool for analyzing data of complex structures.However,their computer-intensive nature,w
会议
The Cancer Genomes Atlas(TCGA) data are unique in that multimodal measurements across genomics features,such as copy number,DNA methylation,and gene expression,
会议
会议