Abstract
Mallows’ models are often convenient initial tool for analyzing a set of rank data. They capture the main structure of the data with only one parameter and could be the basis for further research. However, it is usually unrealistic to expect a one-parameter model to reveal all features of the data. One possible generalization of these models could be made by assuming that there are several latent groups in the population. In this paper, we propose an algorithm to find maximum likelihood estimates of the unknown parameters of Latent Mallows’ models by making use of the EM algorithm. As an application of the considered estimation algorithm, a comparison between models based on different metrics is made via simulation study.
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Acknowledgements
The work of the first author was partly supported by the National Science Fund of Bulgaria under Grant DFNI-I02/19 and by the Support Program of Bulgarian Academy of Sciences for Young Researchers under Grant 17-95/2017. The work of the second author was supported by the National Science Fund of Bulgaria under Grant DH02-13.
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Nikolov, N.I., Stoimenova, E. (2019). EM Estimation of the Parameters in Latent Mallows’ Models. In: Georgiev, K., Todorov, M., Georgiev, I. (eds) Advanced Computing in Industrial Mathematics. BGSIAM 2017. Studies in Computational Intelligence, vol 793. Springer, Cham. https://doi.org/10.1007/978-3-319-97277-0_26
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DOI: https://doi.org/10.1007/978-3-319-97277-0_26
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