Finite Information Limit Variance-Covariance Structures: Is The Entire Dataset Needed For Analysis?

2016 International Conference on High Performance Computing & Simulation (HPCS)(2016)

引用 0|浏览4
暂无评分
摘要
Finite Information Limit (FIL) variance-covariance structures for hierarchical data are introduced and examined: for such data, it is often possible to analyze only a sometimes very small subset, leading to considerable computation time gain, with almost no efficiency loss. A central example is compound-symmetry. A simple approach is proposed to detect this property in a given dataset.
更多
查看译文
关键词
Compound-symmetry,Correlated Data,Data sub-sampling,Fast and parallel computation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要