We have measured 21-cm absorption and emission spectra in the direction of a further 7 southern pulsars with the Parkes telescope to derive their kinematic distances and to study the interstellar medium. For the first time we have successfully obtained H i absorption measurements for PSRs J1602–5100, J1740–3015 and J1745–3040. We have also significantly improved the sensitivity and resolution o...

In some long term studies, a series of dependent and possibly truncated lifetime data may be observed. Suppose that the lifetimes have a common continuous distribution function F. A popular stochastic measure of the distance between the density function f of the lifetimes and its kernel estimate fn is the integrated square error (ISE). In this paper, we derive a central limit theorem for t...

Abstract Distance sampling from aerial platforms can provide researchers with precise and efficient density estimates for wildlife populations, particularly over large areas. The intensity of the distance survey depends on effort. Effort be referred to as coverage, where transect spacing is determined by observer's sightability object or animal. Managers in Texas often use helicopters collect d...

Methods of nearest neighbors are essential in wide range of applications where it is necessary to estimate probability density (e.g. Bayes’s classifier, problems of searching in large databases). This paper contemplates on features of distribution of nearest neighbors’ distances in high-dimensional spaces. It shows that for uniform distribution of points in n-dimensional Euclidean space the dis...

Spatially explicit densities of wildlife are important for understanding environmental drivers populations, and density surfaces intraspecific classes allow exploration links between demographic ratios conditions. Although spatially class valuable, conventional design-based estimators remain prevalent when using camera-trapping methods unmarked populations. We developed a surface model that uti...

A novel nonparametric clustering algorithm is proposed using the interpoint distances between members of data to reveal inherent structure existing in given set data, where we apply classical univariate kernel density estimation method estimate around a member. Our simple its formation and easy resulting well-defined clusters. The starts with objective selection initial cluster representative a...