The aim of this study was to translate and validate the Gratitude Questionnaire (GQ; McCullough et al. 2002) using Taiwanese undergraduate students. A total of 608 college students (Mage = 20.19, SD = 2.08) were recruited for the current study and they completed the GQ, optimism, happiness, and big five personality questionnaires. Confirmation factor analysis indicated that a five item model wa...

In order to choose from the large number of classification methods available for use, cross-validation error estimates are often employed. We present this cross-validation selection strategy in the framework of meta-learning and show that conceptually, metalearning techniques could provide better classifier selections than traditional cross-validation selection. Using various simulation studies...

Likelihood-based cross-validation is a statistical tool for selecting a density estimate based on n i.i.d. observations from the true density among a collection of candidate density estimators. General examples are the selection of a model indexing a maximum likelihood estimator, and the selection of a bandwidth indexing a nonparametric (e.g. kernel) density estimator. In this article, we estab...

We perform a review of model selection procedures, in particular various cross validation procedures and adaptive model selection. We cover important results for these procedures and explore the connections between different procedures and information criteria. INDEX WORDS: Model selection, Adaptive model selection, Cross validation, Information Criteria A REVIEW OF CROSS VALIDATION AND ADAPTIV...

We present a general theory of estimation of analysis error covariances based on 9 cross-validation as well as a geometric interpretation of the method. In particular we use the variance 10 of passive observation–minus-analysis residuals and show that the true analysis error variance can be 11 estimated, without relying on the optimality assumption. This approach is used to obtain near 12 optim...

The \no free lunch" theorems (Wolpert and Macready, 1995) have sparked heated debate in the computational learning community. A recent communication, (Zhu and Rohwer, 1996) attempts to demonstrate the ineeciency of cross-validation on a simple problem. We elaborate on this result by considering a broader class of cross-validation. We show that when used more strictly, cross-validation can yield...

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