Robustness of partial least-squares method for estimating latent variable quality structures
Robustness of partial least-squares method for estimating latent variable quality structures
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DOI：
10.1080/02664769922322
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发表时间：
1999-05-01
影响因子：
1.5
通讯作者：
Westlund, AH
中科院分区：
文献类型：
--
作者：
Cassel, C;Hackl, P;Westlund, AH
潜变量结构模型和偏最小二乘（PLS）估计程序已发现越来越多的兴趣，因为被用于客户满意度测量的背景下。众所周知，内部结构模型的估计是不一致的，这意味着有限样本量的估计是有偏的。一个简化版本的结构模型，用于瑞典客户满意度指数（SCSI）系统已被用来产生模拟数据，并研究PLS算法存在三个不足：（i）斜而不是对称分布的显式变量;（ii）多重共线性块内的显式和潜变量之间;以及（iii）结构模型的错误说明（忽略回归量）。仿真结果表明，PLS方法是相当强大的对这些不足之处。偏置，是由PLS估计的不一致性所造成的大幅增加，只有极偏态分布和错误的遗漏高度相关的潜在回归变量。潜变量的估计得分总是与真实值非常一致，并且似乎不受调查中的不足之处的影响。
Latent variable structural models and the partial least-squares (PLS) estimation procedure have found increased interest since being used in the context of customer satisfaction measurement. The well-known properly that the estimates of the inner structure model are inconsistent implies biased estimates for finite sample sizes. A simplified version of the structural model that is used for the Swedish Customer Satisfaction Index (SCSI) system has been used to generate simulated data and to study the PLS algorithm in the presence of three inadequacies: (i) skew instead of symmetric distributions for manifest variables; (ii) multi-collinearity within blocks of manifest and between latent variables; and (iii) misspecification of the structural model (omission of regressors). The simulation results show that the PLS method is quite robust against these inadequacies. The bias that is caused by the inconsistency of PLS estimates is substantially increased only for extremely skewed distributions and for the erroneous omission of a highly relevant latent regressor variable. The estimated scores of the latent variables are always in very good agreement with the true values and seem to be unaffected by the inadequacies under investigation.