Impact of an equality constraint on the class-specific residual variances in regression mixtures: A Monte Carlo simulation study.
Impact of an equality constraint on the class-specific residual variances in regression mixtures: A Monte Carlo simulation study.
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DOI：
10.3758/s13428-015-0618-8
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发表时间：
2016-06
影响因子：
5.4
通讯作者：
Van Horn ML
中科院分区：
文献类型：
--
作者：
Kim M;Lamont AE;Jaki T;Feaster D;Howe G;Van Horn ML
Regression mixture models are a novel approach for modeling heterogeneous effects of predictors on an outcome. In the model building process residual variances are often disregarded and simplifying assumptions made without thorough examination of the consequences. This simulation study investigated the impact of an equality constraint on the residual variances across latent classes. We examine the consequence of constraining the residual variances on class enumeration (finding the true number of latent classes) and parameter estimates under a number of different simulation conditions meant to reflect the type of heterogeneity likely to exist in applied analyses. Results showed that bias in class enumeration increased as the difference in residual variances between the classes increased. Also, an inappropriate equality constraint on the residual variances greatly impacted estimated class sizes and showed the potential to greatly impact parameter estimates in each class. Results suggest that it is important to make assumptions about residual variances with care and to carefully report what assumptions were made.
