On the Multivariate Gamma-Gamma (ΓΓ) Distribution with Arbitrary Correlation and Applications in Wireless Communications

The statistical properties of the multivariate GammaGamma (ΓΓ) distribution with arbitrary correlation have remained unknown. In this paper, we provide analytical expressions for the joint probability density function (PDF), cumulative distribution function (CDF) and moment generation function of the multivariate ΓΓ distribution with arbitrary correlation. Furthermore, we present novel approximating expressions for the PDF and CDF of the su m of ΓΓ random variables with arbitrary correlation. Based on this statistical analysis, we investigate the performance of radio frequency and optical wireless communication systems. It is noteworthy that the presented expressions include several previous results in the literature as special cases.

Nuclear translocation and functions of growth factor receptors

Cellular signal transduction in response to environmental signals involves a relay of precisely regulated signal amplifying and damping events. A prototypical signaling relay involves ligands binding to cell surface receptors and triggering the activation of downstream enzymes to ultimately affect the subcellular distribution and activity of DNA-binding proteins that regulate gene expression. These so-called signal transduction cascades have dominated our view of signaling for decades. More recently evidence has accumulated that components of these cascades can be multifunctional, in effect playing a conventional role for example as a cell surface receptor for a ligand whilst also having alternative functions for example as transcriptional regulators in the nucleus. This raises new challenges for researchers. What are the cues/triggers that determine which role such proteins play? What are the trafficking pathways which regulate the spatial distribution of such proteins so that they can perform nuclear functions and under what circumstances are these alternative functions most relevant?

Reliable survival analysis based on the Dirichlet Process

We present a robust Dirichlet process for estimating survival functions from samples with right-censored data. It adopts a prior near-ignorance approach to avoid almost any assumption about the distribution of the population lifetimes, as well as the need of eliciting an infinite dimensional parameter (in case of lack of prior information), as it happens with the usual Dirichlet process prior. We show how such model can be used to derive robust inferences from right-censored lifetime data. Robustness is due to the identification of the decisions that are prior-dependent, and can be interpreted as an analysis of sensitivity with respect to the hypothetical inclusion of fictitious new samples in the data. In particular, we derive a nonparametric estimator of the survival probability and a hypothesis test about the probability that the lifetime of an individual from one population is shorter than the lifetime of an individual from another. We evaluate these ideas on simulated data and on the Australian AIDS survival dataset. The methods are publicly available through an easy-to-use R package.