We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Choosing a Model Selection Strategy.
- Authors
Luna, X.D.; Skouras, K.
- Abstract
An important problem in statistical practice is the selection of a suitable statistical model. Several model selection strategies are available in the literature, having different asymptotic and small sample properties, depending on the characteristics of the data generating mechanism. These characteristics are difficult to check in practice and there is a need for a data–driven adaptive procedure to identify an appropriate model selection strategy for the data at hand. We call such an identification a model metaselection, and we base it on the analysis of recursive prediction residuals obtained from each strategy with increasing sample sizes. Graphical tools are proposed in order to study these recursive residuals. Their use is illustrated on real and simulated data sets. When necessary, an automatic metaselection can be performed by simply accumulating predictive losses. Asymptotic and small sample results are presented.
- Subjects
STATISTICS; PROBABILITY theory
- Publication
Scandinavian Journal of Statistics, 2003, Vol 30, Issue 1, p113
- ISSN
0303-6898
- Publication type
Article
- DOI
10.1111/1467-9469.00321