Generating Correlated, Non-normally Distributed Data Using a Non-linear Structural Model
Abstract
An approach to generate non-normality in multivariate data based on a structural model with normally distributed latent variables is presented. The key idea is to create non-normality in the manifest variables by applying non-linear linking functions to the latent part, the error part, or both. The algorithm corrects the covariance matrix for the applied function by approximating the deviance using an approximated normal variable. We show that...
Paper Details
Title
Generating Correlated, Non-normally Distributed Data Using a Non-linear Structural Model
Published Date
Jun 10, 2015
Journal
Volume
80
Issue
4
Pages
920 - 937
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