Clustering Via Nonparametric Density Estimation: the R Package pdfCluster

The R package pdfCluster performs cluster analysis based on a nonparametric estimate of the density of the observed variables. After summarizing the main aspects of the methodology, we describe the features and the usage of the package, and finally illustrate its working with the aid of two datasets

NLOS Mitigation in TOA-based Indoor Localization by Nonlinear Filtering under Skew t-distributed Measurement Noise

Wireless localization by time-of-arrival (TOA) measurements is typically corrupted by non-line-of-sight (NLOS) conditions, causing biased range measurements that can degrade the overall positioning performance of the system. In this article, we propose a localization algorithm that is able to mitigate the impact of NLOS observations by employing a heavy-tailed noise statistical model. Modeling the observation noise by a skew t-distribution allows us to, on the one hand, employ a computationally light sigma-point Kalman filtering method while, on the other hand, be able to effectively characterize the positive skewed non-Gaussian nature of TOA observations under LOS/NLOS conditions. Numerical results show the enhanced performance of such approach

Modelling Body Mass Index Distribution using Maximum Entropy Density

The objective of this paper is to model the distribution of Body Mass Index (BMI) for a given set of covariates. BMI is one of the leading indicators of health and has been studied by health professionals for many years. As such, there have been various approaches to model the distribution of BMI. Furthermore, there are numerous studies which investigate the association between an individual’s physical and socio-economic attributes (covariates) to their BMI levels. This paper proposes the use of Maximum Entropy Density (MED) to model the distribution of BMI using information from covariates. The paper shows how covariates can be incorporated into the MED framework. This framework is then applied to an Australian data set. The results show how different covariates affect different moments of the estimated BMI distribution