I show how one can estimate the shape of a thermal performance curve using information theory. This approach ranks plausible models by their Akaike information criterion (AIC), which is a measure of a model’s ability to describe the data discounted by the model’s complexity. I analyze previously published data to demonstrate how one applies this approach to describe a thermal performance curve....

This paper is concerned with the goodness-of-fit of induced decision trees. Namely, we explore the possibility to measure the goodnessof-fit as it is classically done in statistical modeling. We show how Chisquare statistics and especially the Log-likelihood Ratio statistic that is abundantly used in the modeling of cross tables, can be adapted for induction trees. Not only is the Log-likelihoo...

The selection of an optimal set of parameters from a larger one is a well known identification problem in classification or clustering algorithms. The Akaike criterion has been developed to estimate the (Markov) order in auto regressive models. This criterion, which by itself extends the maximum likelihood method to test composite hypotheses, is replaced by the Modified Information Criterion (M...

Variable selection in linear models is essential for improved inference and interpretation, an activity which has become even more critical for high dimensional data. In this article, we provide a selective review of some classical methods including Akaike information criterion, Bayesian information criterion, Mallow’s Cp and risk inflation criterion, as well as regularization methods including...

Hydrological drought refers to a persistently low discharge and volume of water in streams and reservoirs, lasting months or years. Hydrological drought is a natural phenomenon, but it may be exacerbated by human activities. Hydrological droughts are usually related to meteorological droughts, and their recurrence interval varies accordingly. This study pursues to identify a stochastic model (o...

Based on Kullback-Leibler information we propose a data-driven selector, called GAIC (c) , for choosing parameters of regression splines in nonparametric regression via a stepwise forward/backward knot placement and deletion strategy 1]. This criterion uniies the commonly used information criteria and includes the Akaike information criterion (AIC) 2] and the corrected Akaike information criter...

The problem of model selection — automatically choosing the correct function to describe a data set — is relevant to many areas of computer vision. Many model selection criteria have been used in the vision literature and many more have been proposed in statistics, but the relative strengths of these criteria have not been analyzed in vision. Using the problem of surface reconstruction as our c...

This report describes a line linker based on tools of Frequentist Statistics: Chi-square signiicance testing and the Akaike Information Criterion. The line linker has been implemented as an AVS T M module and is available as freeware. This report describes the basic facts about the distributed linker and its use.

We present a novel inter-camera trajectory association algorithm for partially overlapping visual sensor networks. The approach consists of three steps, namely Extraction, Representation and Association. Firstly, we extract trajectory segments in each camera view independently. These local trajectory segments are then projected on a common-plane. Next, we learn dynamic motion models of the proj...

We present a general methodology to incorporate fundamental economic factors to our previous theory of herding to describe bubbles and antibubbles. We start from the strong form of Rational Expectation and derive the general method to incorporate factors in addition to the log-periodic power law (LPPL) signature of herding developed in ours and others’ works. These factors include interest rate...