background : gastric cancer is the one of the most prevalent reason of cancer-related death in the world. survival of patients after surgery involves identifying risk factors. there are various models to detect the effect of risk factors on patients’ survival. the present study aims at evaluating these models. methods : data from 330 gastric cancer patients diagnosed at the iran cancer institut...

Model selection is the problem of distinguishing competing models, perhaps featuring different numbers of parameters. The statistics literature contains two distinct sets of tools, those based on information theory such as the Akaike Information Criterion (AIC), and those on Bayesian inference such as the Bayesian evidence and Bayesian Information Criterion (BIC). The Deviance Information Crite...

Consider the simple normal linear regression model for estimation/prediction at a new design point. When the slope parameter is not obviously nonzero, hypothesis testing and model selection methods can be used for identifying the right model. We compare performance of such methods both theoretically and empirically from different perspectives for more insight. The testing approach, in spite of ...

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...

Correlated response data are common in biomedical studies. Regression analysis based on the generalized estimating equations (GEE) is an increasingly important method for such data. However, there seem to be few model-selection criteria available in GEE. The well-known Akaike Information Criterion (AIC) cannot be directly applied since AIC is based on maximum likelihood estimation while GEE is ...

Prof. Akaike made significant contributions in various fields of statistical science, in particular, in time series analysis in frequency domain and time domain, information criterion and Bayes modeling. In this article, his research contributions are described in order of launching period, frequency time domain analysis, time domain time series modeling, AIC and statistical modeling, and Bayes...

Reversible-jump Markov chain Monte Carlo (RJ-MCMC) is a technique for simultaneously evaluating multiple related (but not necessarily nested) statistical models that has recently been applied to the problem of phylogenetic model selection. Here we use a simulation approach to assess the performance of this method and compare it to Akaike weights, a measure of model uncertainty that is based on ...

Globular clusters are cosmological fossils, revealing the history of the universe and galaxies. Some galaxies are found to have multiple generations of globular clusters. Traditionally, these multiple epochs of cluster formation have been detected through graphical methods. In this presentation, we discuss statistical model selection to determine the number of components in globular cluster sys...