散点矩阵一致性：数据子集上回归的诊断
Scatter Matrix Concordance: A Diagnostic for Regressions on Subsets of Data
摘要：
线性回归模型直接取决于设计矩阵及其属性。通过划分设计矩阵的行来有效地估计模型系数的技术在大规模问题中越来越受欢迎，因为它们与现代并行计算架构非常匹配。我们提出了一个简单的措施{ \em一致性}之间的设计矩阵和一个子集的行，估计如何以及一个子集捕捉方差-协方差结构的一个更大的数据集。我们说明了使用这种措施的启发式方法选择行分区的大小，平衡在现实世界中的问题的统计和计算效率的目标。
英文摘要：
Linear regression models depend directly on the design matrix and its properties. Techniques that efficiently estimate model coefficients by partitioning rows of the design matrix are increasingly popular for large-scale problems because they fit well with modern parallel computing architectures. We propose a simple measure of {\em concordance} between a design matrix and a subset of its rows that estimates how well a subset captures the variance-covariance structure of a larger data set. We illustrate the use of this measure in a heuristic method for selecting row partition sizes that balance statistical and computational efficiency goals in real-world problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
