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 ...

Using an innovations state space approach, it has been found that the Akaike information criterion (AIC) works slightly better, on average, than prediction validation on withheld data, for choosing between the various common methods of exponential smoothing for forecasting. There is, however, a puzzle. Should the count of the seed states be incorporated into the penalty term in the AIC formula?...

This lecture discusses the role of language and information theory concepts for data compression and solving the inverse problem. The concept of Algorithmic Information Content (AIC) is introduced and shown to be crucial to achieving optimal data compression and optimized Bayesian priors for image reconstruction. The dependence of the AIC on the selection of language then suggests how e±cient c...

Given that one-sided hypothesis tests are more powerful than their two-sided counterparts, model selection procedures that employ one-sided information should outperform those that do not. In the frequentist area of econometrics, perhaps the most popular model selection procedures are those based on estimates of the Kullback-Leibler information, Akaike’s (1973) Information Criterion (AIC) being...

The information of first-arrival time of acoustic emission (AE) signal is important in event location, event identification and source mechanism analysis. Manual picks are time-consuming and sometimes subjective. Several approaches are used in practice. New first arrival automatic determination technique of AE signals in thin metal plates is presented. Based on Akaike information criterion (AIC...

Model-selection criteria such as AIC and BIC are widely used in applied statistics. In recent years, there has been a huge increase in modeling data from large complex surveys, and a resulting demand for versions of AIC and BIC that are valid under complex sampling. In this paper, we show how both criteria can be modified to handle complex samples. We illustrate with two examples, the first usi...

Methodologists have criticized the use of significance tests in the behavioral sciences but have failed to provide alternative data analysis strategies that appeal to applied researchers. For purposes of comparing alternate models for data, information-theoretic measures such as Akaike AIC have advantages in comparison with significance tests. Model-selection procedures based on a min(AIC) stra...

We apply the nonconcave penalized likelihood approach to obtain variable selections as well as shrinkage estimators. This approach relies heavily on the choice of regularization parameter, which controls the model complexity. In this paper, we propose employing the generalized information criterion (GIC), encompassing the commonly used Akaike information criterion (AIC) and Bayesian information...

The purpose of this study is to develop an efficient appraoch for producing hyperspectral images by using reconstructed spectral reflectance from multispectral images. In this study, an indirect reconstruction based on regression analysis was employed because of its stability to noise and its practicality. In this approach however, the regression model selection and channel selection when acqui...