Density ratio model selection

Fokianos, K. (2007) Density ratio model selection. Journal of Statistical Computation and Simulation, 77 (9). pp. 805-819. ISSN 0094-9655

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Abstract

The density ratio model presumes that the log-likelihood ratio of two unknown densities is of some known parametric linear form. However, the choice of the functional form has an impact on both estimation and testing. The problem of over/underfitting in the context of the density ratio model is examined and the theory shows that bias and loss of efficiency are introduced when the model is misspecified. The problem of identifying the appropriate functional form for an application of the density ratio model is addressed by means of model selection criteria, which perform reasonably well. Several simulations integrate the presentation.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Statistical Computation and Simulation
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
127883
Deposited By:
Deposited On:
03 Oct 2018 08:40
Refereed?:
Yes
Published?:
Published
Last Modified:
11 Jun 2020 07:11