We apply a recently proposed Bayesian model selection technique, known as stochastic model specification search, for …

non-nested model selection tool in a similar fashion to the Rivers and Vuong (2002) framework. For this purpose, we first …, in Chapter 4, we use the model selection methodology used in Chapter 1 for comparing different formulations of the pure … hypothesis of correct specification. Further, we study the asymptotic distribution of functionals of this statistic for model …

is how biased estimation affects model selection. In this paper, we introduce biased estimation to a range of model … and unbiased estimation and compare it against modern model selection criteria such as Kay's conditional model order … estimator (CME), the bootstrap and the more recently proposed hook-and-loop resampling based model selection. The advantages and …

derived. We also propose a Bayesian model selection criterion for choosing between the unconstrained and the constrained … illustrated with both simulated data and real applications. An R package COZIGAM has been developed for model fitting and model … selection with zero-inflated data. …

easily complicate the model selection problem. Many useful procedures have been developed to estimate parameters and standard … the data is incomplete.In this dissertation, we propose model selection criteria based on the Kullback-Leiber discrepancy …