The statistical distributions are modeled in explaining nature of various types of data sets. Although these distributions are mostly uni-modal, it is quite common to see multiple modes in the observed distribution of the underlying variables, which make the precise modeling unrealistic. The observed data do not exhibit smoothness not necessarily due to randomness, but could also be due to non-...

BACKGROUND We introduce a new criterion, the percentile inclusion probability, for comparing methods for calculating reference intervals. The criterion is compared with a previously published measure of reliability suggested by Linnet (Linnet K. Clin Chem 1987;33:381-6), the ratio of the width of the confidence interval for the percentile to that of the reference interval. METHODS Data were s...

Upcoming interferometric detectors of gravitational waves will generate data at large rates over a long time. The data will consist of not only the strain measurement but also the output of system and environmental monitors. Tests for detecting non-stationarity that require a statistical model for the ambient noise may not be the right choice for analyzing this huge data set since, in most case...

One of the least understood qualities of radon and its associated risk is the relationship between its concentration at the source and the statistical distribution of concentrations found in indoor environments. In this presentation I will clarify several of the important factors and provide a simple statistical model for discussing and communicating distribution qualities of our primary source...

The objective of this study was to evaluate the ability of three statistical distributions to characterize the vertical distribution of foliage mass in canopies of even-aged loblolly pine stands, based on airborne-scanning light detection and ranging (LiDAR) data. The functions were the Weibull and SB distributions and a mixture of the lognormal and Weibull distributions. Results indicated that...