conclusions the use of smoothing methods helps us to eliminate non-linear effects but it is more appropriate to use cox proportional hazards model in medical data because of its’ ease of interpretation and capability of modeling both continuous and discrete covariates. also, cox proportional hazards model and smoothing methods analysis identified that age at diagnosis and tumor size were indepe...

We develop a version of the Corrected Akaike Information Criterion (AICC) suitable for selection of an h-step-ahead linear predictor for a weakly stationary time series in discrete time. A motivation for this criterion is provided in terms of a generalized Kullback-Leibler information which is minimized at the optimal h-step predictor, and which is equivalent to the ordinary Kullback-Leibler in...

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...

To address order selection for an autoregressive model fitted to time series data, we propose a new information criterion. It has the benefits of the two wellknown model selection techniques, the Akaike information criterion and the Bayesian information criterion. When the data is generated from a finite order autoregression, the Bayesian information criterion is known to be consistent, and so ...