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EnglishIn this paper, we propose several initial values for the EM algorithm of maximum likelihood estimates of the parameters in a class of models, called CUB, recently introduced for ordinal data. Specifically, we compare the algorithmic efficiency of each estimator with respect to a naive proposal through a vast simulation experiment. The results confirm a substantial gain in efficiency of the moments estimators over the whole parametric space. Then, some extensions are also discussed and several applications to real data sets are presented.
| Titolo: | A comparison of preliminary estimators in a class of ordinal data models | |
| Autori: | ||
| Data di pubblicazione: | 2009 | |
| Rivista: | ||
| Abstract: | In this paper, we propose several initial values for the EM algorithm of maximum likelihood estim...ates of the parameters in a class of models, called CUB, recently introduced for ordinal data. Specifically, we compare the algorithmic efficiency of each estimator with respect to a naive proposal through a vast simulation experiment. The results confirm a substantial gain in efficiency of the moments estimators over the whole parametric space. Then, some extensions are also discussed and several applications to real data sets are presented. | |
| Handle: | http://hdl.handle.net/11588/353654 | |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
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