The geometric theory of ignorance [1] suggests new criteria for model selection. One example is to choose model M minimizing, CIC = − N ∑ i=1 log p̂(xi)+ d 2 log N 2π + logV + πR N log(d + 1) where (x1, . . . ,xN) is a sample of N iid observations, p̂ ∈ M is the mle, d = dim(M) is the dimension of the model M, V = Vol(M) is its information volume and R = Ricci(M) is the Ricci scalar evaluated at ...

The idea of using the accumulated prediction error, or APE, as a model selection criterion was originally proposed in the time series context. This article attempts to extend the use of APE to the comparison of linear models for panel data. We propose a generalized APE, or GAPE, assuming that the goal of modeling is to predict an aggregate statistic. We argue that in panel data analysis, it is ...

Many non-linearity tests have been developed to apply on specific time series, but non of the tests has proved uniformly most powerful; depending on the situation another test may perform better in distinguishing between truly linear and non-linear series. In this context, Peña and Rodriguez circumvent the philosophical question as to the nature of non-linearity in order to find and develop a n...

miRNA155 has been implicated in normal T cell function and their differentiations into the Th1 subtype. We have shown that acute alcohol (ethanol) intoxication combined with burn injury suppresses T cell IFN-γ release. Herein, we examined whether the decrease in IFN-γ is resulted from altered expression of miRNA155 and transcription factors--NFAT, Tbx21, Jun and Fos--in T cells following ethano...

BACKGROUND Graphical models were identified as a promising new approach to modeling high-dimensional clinical data. They provided a probabilistic tool to display, analyze and visualize the net-like dependence structures by drawing a graph describing the conditional dependencies between the variables. Until now, the main focus of research was on building Gaussian graphical models for continuous ...

Given a paremetric model, the task of statistical learning consists of a parameter learning part for determining unknown parameters and a model selection part for selecting an appropriate scale for a model that accommodates these parameters. Typically, the two tasks are implemented in a twophase procedure. First, a number of models of a same architecture but in different scales are enumerated, ...

OBJECTIVES The number of depression symptoms can be considered as count data in order to get complete and accurate analyses findings in studies of depression. This study aims to compare the goodness of fit of four count outcomes models by a large survey sample to identify the optimum model for a risk factor study of the number of depression symptoms. METHODS 15 820 subjects, aged 10 to 80 yea...

In this simple note, we attempt to further improve the sparse principal component analysis (SPCA) of Zou et al. (2006) on the following two aspects. First, we replace the traditional lasso penalty utilized in the original SPCA by the most recently developed adaptive lasso penalty (Zou, 2006; Wang et al., 2006). By doing so, adaptive amounts of shrinkage can be applied to different loading coeff...

Abstract We consider N independent stochastic processes (Xi(t), t ∈ [0, Ti]), i = 1, . . . , N , defined by a stochastic differential equation with drift term depending on a random variable φi. The distribution of the random effect φi is a Gaussian mixture distribution, depending on unknown parameters which are to be estimated from the continuous observation of the processes Xi. The likelihood ...

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 ...