The purpose of this paper is to find the estimate of the parameters of k and g   distribution for ranked set sample by numerically maximizing the likelihood function. The estimates named as numerical maximum likelihood estimate, and corresponding mean square error and relative efficiency compared to simple random sampling are computed using a computer simulation. Ranked set sampling is seen t...

We study the quasi-random choice method (QRCM) for the Liouville equation of geometrical optics with discontinuous local wave speed. This equation arises in the phase space computation of high frequency waves through interfaces, where waves undergo partial transmissions and reflections. The numerical challenges include interface, contact discontinuities, and measure-valued solutions. The so-cal...

Low-rank matrix completion (LRMC) problems arise in a wide variety of applications. Previous theory mainly provides conditions for completion under missing-at-random samplings. This paper studies deterministic conditions for completion. An incomplete d ×N matrix is finitely rank-r completable if there are at most finitely many rank-r matrices that agree with all its observed entries. Finite com...

This paper explores the compressibility of complex systems by considering the simplification of Boolean networks. A method, which is similar to that reported by Bastolla and Parisi,4,5 is considered that is based on the removal of frozen nodes, or stable variables, and network “leaves,” i.e. those nodes with outdegree = 0. The method uses a random sampling approach to identify the minimum set o...

the purpose of this study was to compare symptoms of ptsd among war affected people and employees in the fire station. totally, 60 people affected by war and 60 fire workers were selected using simple random sampling. by completing pcl inventory, samples of ptsd were characterized. data was analysed through spss software by independent t-test. as the results showed, severity of ptsd symptoms am...

We conduct a detailed simulation study to assess how well various sampling techniques recover network characteristics such as degree, clustering coefficient, and path length distributions of several simulated population networks that have the high clustering tendency characteristic of social networks but vary in terms of degree distribution and density. We consider several alternative sampling ...

The size of large, geo-located datasets has reached scales where visualization of all data points is inefficient. Random sampling is a method to reduce the size of a dataset, yet it can introduce unwanted errors. We describe a method for subsampling of spatial data suitable for creating kernel density estimates from very large data and demonstrate that it results in less error than random sampl...