The Journal of Statistical Distributions and Applications is a peer-reviewed international journal published under the brand SpringerOpen. The scope includes, but is not limited to, the development and study of statistical distributions; frequentist and Bayesian statistical inference, including goodness-of-fit tests; statistical modeling; computational/simulation methods; and data analysis related to statistical distributions. Fully open access, the Journal is devoted to advancing understanding of theory and methods in the area of statistical distributions and their applications.
Journal of Statistical Distributions and Applications
Description
Identifiers
e-ISSN | 2195-5832 |
Publisher
Springer Berlin Heidelberg
Additional information
Data set: Springer
Articles
Journal of Statistical Distributions and Applications > 2019 > 6 > 1 > 1-18
The class of (p1,…,pk)-spherical probability laws and a method of simulating random vectors following such distributions are introduced using a new stochastic vector representation. A dynamic geometric disintegration method and a corresponding geometric measure representation are used for generalizing the classical χ2-, t- and F-distributions. Comparing the principles of specialization and marginalization...
Journal of Statistical Distributions and Applications > 2019 > 6 > 1 > 1-24
Many systems experience gradual degradation while simultaneously being exposed to a stream of random shocks of varying magnitudes that eventually cause failure when a shock exceeds the residual strength of the system. In this paper, we present a family of stochastic processes, called shock-degradation processes, that describe this failure mechanism. In our failure model, system strength follows a...
Journal of Statistical Distributions and Applications > 2019 > 6 > 1 > 1-17
Various applications in natural science require models more accurate than well-known distributions. In this context, several generators of distributions have been recently proposed. We introduce a new four-parameter extended normal (EN) distribution, which can provide better fits than the skew-normal and beta normal distributions as proved empirically in two applications to real data. We present Monte...