Disjoint factor analysis with cross-loadings
Disjoint factor analysis with cross-loadings
复制标题
具有交叉载荷的不相交因子分析
DOI：
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发表时间：
2016
影响因子：
1.6
通讯作者：
M. Vichi
中科院分区：
文献类型：
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作者：
M. Vichi
不相交因子分析（Disjoint factor analysis， DFA）是我们提出的一种新的潜在因子模型，用于识别与变量的不相交子集相关的因子，从而简化加载矩阵结构。与探索性因素分析（EFA）类似，DFA不假设因素数量的先验信息以及变量和因素之间的相关关系。在DFA中，在适当的变量排列后，总体方差-协方差结构是假设的块对角结构，并使用坐标下降型算法通过极大似然估计。给出了参数对因子数量的推断，并对假设的简单结构进行了验证。该方法能满足载荷的尺度等变性、唯一性、最优简化等特性。如果从最佳DFA解决方案中检测到相关的交叉负载，也会对其进行估计。DFA还可以选择将变量约束在预先指定的因素上，以便研究人员可以先验地假设变量和负载之间的一些关系。模拟研究显示了DFA的性能，并使用一个最佳识别福祉维度的应用程序来说明新方法的特点。最后的讨论结束了本文。
Disjoint factor analysis (DFA) is a new latent factor model that we propose here to identify factors that relate to disjoint subsets of variables, thus simplifying the loading matrix structure. Similarly to exploratory factor analysis (EFA), the DFA does not hypothesize prior information on the number of factors and on the relevant relations between variables and factors. In DFA the population variance–covariance structure is hypothesized block diagonal after the proper permutation of variables and estimated by Maximum Likelihood, using an Coordinate Descent type algorithm. Inference on parameters on the number of factors and to confirm the hypothesized simple structure are provided. Properties such as scale equivariance, uniqueness, optimal simplification of loadings are satisfied by DFA. Relevant cross-loadings are also estimated in case they are detected from the best DFA solution. DFA has also the option to constrain a variable to load on a pre-specified factor so that the researcher can assume, a priori, some relations between variables and loadings. A simulation study shows performances of DFA and an application to optimally identify the dimensions of well-being is used to illustrate characteristics of the new methodology. A final discussion concludes the paper.
影响因子：
7.6
作者：
A. Elliot;Holly Mcgregor
通讯作者：
A. Elliot;Holly Mcgregor