Dynamic logistic regression and dynamic model averaging for binary classification.

ABSTRACT: We propose an online binary classification procedure for cases when there is uncertainty about the model to use and parameters within a model change over time. We account for model uncertainty through dynamic model averaging, a dynamic extension of Bayesian model averaging in which posterior model probabilities may also change with time. We apply a state-space model to the parameters of each model and we allow the data-generating model to change over time according to a Markov chain. Calibrating a "forgetting" factor accommodates different levels of change in the data-generating mechanism. We propose an algorithm that adjusts the level of forgetting in an online fashion using the posterior predictive distribution, and so accommodates various levels of change at different times. We apply ou

SUBMITTER: McCormick TH 

PROVIDER: S-EPMC3831847 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature