title: Model selection in non-nested hidden Markov models for ion channel gating.
abstract: An important task in the application of Markov models to the analysis of ion channel data is the determination of the correct gating scheme of the ion channel under investigation. Some prior knowledge from other experiments can reduce significantly the number of possible models. If these models are standard statistical procedures nested like likelihood ratio testing, provide reliable selection methods. In the case of non-nested models, information criteria like AIC, BIC, etc., are used. However, it is not known if any of these criteria provide a reliable selection method and which is the best one in the context of ion channel gating. We provide an alternative approach to model selection in the case of non-nested models with an equal number of open and closed states. The models to choose from are embedded in a properly defined general model. Therefore, we circumvent the problems of model selection in the non-nested case and can apply model selection procedures for nested models.
authors.affiliation: Freiburger Zentrum für Datenanalyse und Modellbildung, Eckerstr. 1, Freiburg, D-79104, Germany.
authors.forename: M
authors.initials: M
authors.lastname: Wagner
authors.forename: J
authors.initials: J
authors.lastname: Timmer
mesh_terms: D000818:Animals; D003198:Computer Simulation; D015640:Ion Channel Gating / Q000502:physiology*; D007473:Ion Channels / Q000378:metabolism; D008390:Markov Chains; D008954:Models, Biological
chemical_list: D007473:Ion Channels