| dc.creator | Almodóvar, Israel | |
| dc.creator | Pericchi Guerra, Luis R. | |
| dc.date | 2012-11-15T14:04:55Z | |
| dc.date | 2012-11-15T14:04:55Z | |
| dc.date | 2012 | |
| dc.date.accessioned | 2017-03-17T16:53:46Z | |
| dc.date.available | 2017-03-17T16:53:46Z | |
| dc.identifier | http://hdl.handle.net/10586 /256 | |
| dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/647406 | |
| dc.description | The size of a training sample in Objective Bayesian Testing and Model Selection is a central problem
in the theory and in the practice. We concentrate here in simulated training samples and in simple
hypothesis. The striking result is that even in the simplest of situations, the optimal training sample
M, can be minimal (for the identication of the sampling model) or maximal (for optimal prediction of
future data). We suggest a compromise that seems to work well whatever the purpose of the analysis:
the 5% cubic root rule: M = min[0.05 * n, ³√n]. We proceed to define a comprehensive loss function
that combines identication errors and prediction errors, appropriately standardized. We find that the
very simple cubic root rule is extremely close to an over- all optimum for a wide selection of sample sizes
and cutting points that define the decision rules. The first time that the cubic root has been proposed
is in Pericchi (2010). This article propose to generalize the rule and to take full statistical advantage for
realistic situations. Another way to look at the rule, is as a synthesis of the rationale that justify both
AIC and BIC. | |
| dc.language | en_US | |
| dc.subject | 5% cubic root rule | |
| dc.subject | intrinsic priors | |
| dc.subject | Objective Bayesian Hypothesis testing | |
| dc.subject | training sample | |
| dc.title | New Criteria for the Choice of Training Sample Size for Model Selection and Prediction: The Cubic Root Rule | |
| dc.type | Otro | |
