Two theorems and a lemma are presented about the use of jackknife estimator and the cross-validation method for model selection. Theorem 1 gives the asymptotic form for the jackknife estimator. Combined with the model selection criterion, this asymptotic form can be used to obtain the fit of a model. The model selection criterion we used is the negative of the average predictive likehood, the c...

strategic selection problem is a multi-criteria problem which includes conflicting tangible and intangible criteria. in order to select the most appropriate strategic alternative it is necessary to make tradeoffs between these criteria as well as taking into account the resource limitations which may exist. available methods neglect the distance concept which exists between the alternatives wei...

This paper considers optimization of the ridge parameters in generalized ridge regression (GRR) by minimizing a model selection criterion. GRR has a major advantage over ridge regression (RR) in that a solution to the minimization problem for one model selection criterion, i.e., Mallows’ Cp criterion, can be obtained explicitly with GRR, but such a solution for any model selection criteria, e.g...

. This paper proposes a new complexity-penalization model selection strategy derived from the minimum risk principle and the behavior of candidate models under noisy conditions. This strategy seems to be robust in small sample size conditions and tends to AIC criterion as sample size grows up. The simulation study at the end of the paper will show that the proposed criterion is extremely compet...

The mean-variance theory of Markowitz (1952) indicates that large investment portfolios naturally provide better risk diversification than small ones. However, due to parameter estimation errors, one may find ambiguous results in practice. Hence, it is essential to identify relevant stocks to alleviate the impact of estimation error in portfolio selection. To this end, we propose a linkage cond...

Riassunto La scelta tra modelli autoregressivi spaziali richiede non solo l’individuazione delle coordinate non nulle di un modello “saturo”, ma anche la specificazione della struttura di vicinato tra le osservazioni. Si considera un criterio di scelta di tipo BIC, basato su una funzione di pseudo-verosimiglianza penalizzata. Il criterio è (debolmente) consistente sotto ipotesi assai generali. ...

COTS systems selection consists in evaluating the user requirements with respect to characteristics of candidate systems, using a set of criteria. One criterion that has received little attention is what we call conceptual fit. The criterion assesses the fit between the conceptual structure of the user requirements and that of a system. We evaluate the fit in terms of the existing misfits. We f...

The problem of model selection is considerably important for acquiring higher levels of generalization capability in supervised learning. In this article, we propose a new criterion for model selection, the subspace information criterion (SIC), which is a generalization of Mallows's C(L). It is assumed that the learning target function belongs to a specified functional Hilbert space and the gen...

Controlling selection bias, a statistical error caused by preferential sampling of data, is a fundamental problem in machine learning and statistical inference. This paper presents a simple criterion for controlling selection bias in the odds ratio, a widely used measure for association between variables, that connects the nature of selection bias with the graph modeling the selection mechanism...