A0792
Title: A metric based on the efficient determination criterion
Authors: Veronica Andrea Gonzalez-Lopez - University of Campinas (Brazil) [presenting]
Abstract: The concept of metrics based on the Bayesian information criterion (BIC) is extended to achieve a strongly consistent estimation of partition Markov models (PMMs). A set of metrics is introduced, drawn from the family of model selection criteria known as efficient determination criteria (EDC). This generalization extends the range of options available in BIC for penalizing the number of model parameters. The relationship is formally specified and determines how EDC works when selecting a model based on a threshold associated with the metric. Furthermore, the penalty options are improved within EDC, identifying the penalty ln(ln(n)) as a viable choice that maintains the strongly consistent estimation of a PMM. To demonstrate the utility of these new metrics, those are applied to the modeling of three DNA sequences of dengue virus type 3, endemic in Brazil in 2023.
