conclusions parametric models may provide complementary data for clinicians and researchers about how risks vary over time. the weibull model seemed to show the best fit among the parametric models of the survival of hemodialysis patients. results the results of a multivariate analysis of the variables in the parametric models showed that the mean serum albumin and the clinic attended were the ...

The Akaike (1973, 1974) information criterion, AIC, and the corrected Akaike information criterion (Hurvich and Tsai, 1989), AICc, were both designed as estimators of the expected Kullback-Leibler discrepancy between the model generating the data and a tted candidate model. AIC is justiied in a very general framework, and as a result, ooers a crude estimator of the expected discrepancy: one whi...

Order-selection criteria for vector autoregressive (AR) modeling are discussed. The performance of an order-selection criterion is optimal if the model of the selected order is the most accurate model in the considered set of estimated models: here vector AR models. Suboptimal performance can be a result of underfit or overfit. The Akaike information criterion (AIC) is an asymptotically unbiase...

This article considers the problem of order selection of the vector autoregressive moving-average models and of the sub-class of the vector autoregressive models under the assumption that the errors are uncorrelated but not necessarily independent. We propose a modified version of the AIC (Akaike information criterion). This criterion requires the estimation of the matrice involved in the asymp...

1: In this paper we discuss measures of information and divergence and model selection criteria. Three classes of measures, Fisher-type, divergencetype and entropy-type measures, are discussed and their properties are presented. Information through censoring and truncation is presented and model selection criteria are investigated including the Akaike Information Criterion (AIC) and the Diverge...

Bayesians often reject the Akaike Information Criterion (AIC) because it introduces ideas that do not fit into their philosophy of statistical inference. Here we show that a difference in the AIC scores that two models receive is evidence that they differ in their degrees of predictive accuracy, where evidence is understood in terms of the Law of Likelihood. Since the Law of Likelihood is a cen...

The Akaike Information Criterion, AIC (Akaike, 1973), and a bias-corrected version, Aicc (Sugiura, 1978; Hurvich & Tsai, 1989) are two methods for selection of regression and autoregressive models. Both criteria may be viewed as estimators of the expected Kullback-Leibler information. The bias of AIC and AICC is studied in the underfitting case, where none of the candidate models includes the t...

We derive and investigate a variant of AIC, the Akaike information criterion, for model selection in settings where the observed data is incomplete. Our variant is based on the motivation provided for the PDIO (\predictive divergence for incomplete observation models") criterion of Shimodaira variant diiers from PDIO in its \goodness-of-t" term. Unlike AIC and PDIO, which require the computatio...

A popular model selection approach for generalized linear mixed-effects models is the Akaike information criterion, or AIC. Among others, [5] pointed out the distinction between the marginal and conditional inference depending on the focus of research. The conditional AIC was derived for the linear mixed-effects model which was later generalized by [4]. We show that the similar strategy extends...

A typical approach to the problem of selecting between models of differing complexity is to choose the model with the minimum Akaike Information Criterion (AIC) score. This paper examines a common scenario in which there is more than one candidate model with the same number of free parameters which violates the conditions under which AIC was derived. The main result of this paper is a novel upp...