On Some Assumptions of the Null Hypothesis Statistical Testing.

Bayesian and classical statistical approaches are based on different types of logical principles. In order to avoid mistaken inferences and misguided interpretations, the practitioner must respect the inference rules embedded into each statistical method. Ignoring these principles leads to the paradoxical conclusions that the hypothesis [Formula: see text] could be less supported by the data than a more restrictive hypothesis such as [Formula: see text], where [Formula: see text] and [Formula: see text] are two population means.

A non-parametric statistical test to compare clusters with applications in functional magnetic resonance imaging data.

Statistical inference of functional magnetic resonance imaging (fMRI) data is an important tool in neuroscience investigation. One major hypothesis in neuroscience is that the presence or not of a psychiatric disorder can be explained by the differences in how neurons cluster in the brain. Therefore, it is of interest to verify whether the properties of the clusters change between groups of patients and controls.

Functional clustering of time series gene expression data by Granger causality.

Background: A common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Clustered gene expression profiles point to the joint contribution of groups of genes to a particular cellular process. However, since genes belong to intricate networks, other features, besides comparable expression patterns, should provide additional information for the identification of functionally similar genes.

A fast and robust statistical test based on likelihood ratio with Bartlett correction to identify Granger causality between gene sets.

Unlabelled: We propose a likelihood ratio test (LRT) with Bartlett correction in order to identify Granger causality between sets of time series gene expression data. The performance of the proposed test is compared to a previously published bootstrap-based approach. LRT is shown to be significantly faster and statistically powerful even within non-Normal distributions.

The impact of measurement errors in the identification of regulatory networks.

Background: There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise.

Results: This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks.