Composite likelihood estimation for Thurstonian models with ranking data
(English)Manuscript (preprint) (Other academic)
A composite likelihood estimation based on trinary rankings (TCL) is developed for Thurstonian models with ranking data. The merits of the proposed method are that: it gives an asymptotically unbiased, consistent, and normally distributed estimator; it is of low computationally complexity regardless of the model size; it estimates all model parameters in a single step; and it does not require the estimation of a weight matrix to compute correct standard errors. Via a simulation study, the performance of the proposed method is evaluated in terms of relative bias and relative mean squared error (MSE) and in comparison with the performance of the three-stage robust diagonally weighted least squares (3S-RDWLS) and three-stage robust unweighted least squares (3S-RULS), both as applied within structural equation models (SEM) with ordinal variables. The simulation results indicate that TCL yields estimates and standard errors with very small relative bias and small MSE even for small sample sizes and large models. Both relative bias and MSE are decreasing with increases in the sample size. Moreover, TCL performs similarly to 3S-RULS and 3S-RDWLS.
unrestricted Thurstonian model, ranking data, composite maximum likelihood, estimation, trinary rankings
Research subject Statistics
IdentifiersURN: urn:nbn:se:uu:diva-187599OAI: oai:DiVA.org:uu-187599DiVA: diva2:577660