A regression equation for determining the dimensionality of data
A regression equation for determining the dimensionality of data
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DOI：
10.1207/s15327906mbr3504_02
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发表时间：
2000-01-01
影响因子：
3.8
通讯作者：
Keeling, KB
中科院分区：
文献类型：
--
作者：
Keeling, KB
并行分析作为一种利用特征值来确定数据维度的标准，得到了广泛的支持和关注。并行分析将样本特征值与由独立正态分布随机变量生成的相关矩阵中的样本的预期特征值进行比较。为了使研究人员更容易进行并行分析，一些研究提出了多元回归方程来估计样本相关矩阵的特征值的期望值，假设总体相关矩阵是单位矩阵。本文提出了一种新的估计特征值均值的回归方程，并与已发表的回归方程进行了比较研究，结果表明该回归方程具有良好的性能。这种提议的技术的优点是不需要列出每个本征根的回归系数的系数表。
Parallel analysis has received much support and attention as a criterion for using eigenvalues to determine the dimensionality of data. Parallel analysis compares sample eigenvalues to expected eigenvalues of a sample from a correlation matrix generated by independent normally distributed random variables. To make parallel analysis more accessible to researchers, several studies have proposed multiple regression equations for estimating the expected value of the eigenvalues of a sample correlation matrix assuming that the population correlation matrix is the identity matrix. A new regression equation to estimate the mean value of eigenvalues is presented in this article and a comparative study reveals favorable performance of this proposed equation to previously published regression equations. This proposed technique has the advantage that a table of coefficients, listing regression coefficients for each eigenvalue root, is not needed.