The sampling method has been paid much attention in the field of complex network in general and statistical physics in particular. This paper presents two new sampling methods based on the perspective that a small part of vertices with high node degree can possess the most structure information of a network. The two proposed sampling methods are efficient in sampling the nodes with high degree....

BACKGROUND The National Children's Study (NCS) was established as a national probability sample of births to prospectively study children's health starting from in utero to age 21. The primary sampling unit was 105 study locations (typically a county). The secondary sampling unit was the geographic unit (segment), but this was subsequently perceived to be an inefficient strategy. METHODS AND ...

This paper describes the use of stochastic simulation techniques to reconstruct biomedical signals not directly measurable. In particular, a deconvolution problem with an uncertain clearance parameter is considered. The problem is addressed using a Monte Carlo Markov Chain method, called the Gibbs Sampling, in which the joint posterior probability distribution of the stochastic parameters is es...

The objective of this paper is to provide readers with the program to estimate a Markov switching model with time varying transition probability(Filardo, 1994) by using a statistical computing software R. Although many of the previous studies estimating the model have conducted the estimation by the maximum likelihood estimation, this paper utilizes Gibbs sampling method. Using Gibbs sampling m...

An empirical study has been carried out to decide about the performance of various estimators used in unequal probability sampling without replacement and a sample of size 2. The Hansen–Hurwitz estimator and simple random sampling method has also been compared in this study. Some suggestions have been given at the end.

This paper describes the use of Monte Carlo sampling for tomographic image reconstruction. We describe an efficient sampling strategy, based on the Riemannian Manifold Markov Chain Monte Carlo algorithm, that exploits the peculiar structure of tomographic data, enabling efficient sampling of the high-dimensional probability densities that arise in tomographic imaging. Experiments with positron ...

We introduce a path sampling method for obtaining statistical properties of an arbitrary stochastic dynamics. The method works by decomposing a trajectory in time, estimating the probability of satisfying a progress constraint, modifying the dynamics based on that probability, and then reweighting to calculate averages. Because the progress constraint can be formulated in terms of occurrences o...

Logistic regression is an important tool for wildlife habitat-selection studies, but the method frequently has been misapplied due to an inadequate understanding of the logistic model, its interpretation, and the influence of sampling design. To promote better use of this method, we review its application and interpretation under 3 sampling designs: random, case–control, and use–availability. L...

tatistical inference allows one to draw conclusions about the characteristics of a population on the basis of data collected from a sample of subjects from that population. Almost all the statistical inferences typically seen in the medical literature are based on probability models that connect summary statistics calculated using the observed data to estimates of parameter values in the popula...