Special Issue on Statistical Modeling and Computation

Statistical models, typically consisting of a collection of probability distributions, are used to describe patterns of variability that random variables or data may display. Describing the invariance of such models is often done via group theory. Although the mathematical notion of a group is relatively simple, the ideas of group theory provide a very convenient way to describe how statistical models change when random variables are transformed. The goal of this special issue is to provide a platform for scientists and academicians all over the world to promote, share, and discuss various new issues and developments in the area of statistical modeling and computation.

In this special issue, we intend to invite front-line researchers and authors to submit original research and review articles on exploring statistical modeling and computation. In this special issue, potential topics include, but are not limited to:

• Probability models
• Generalized linear model
• Multivariate statistics model
• Bayesian model; Markov chain model
• State-space model; Gaussian models
• Monte Carlo methods
• Modern statistical computation techniques

Authors should read over the journal’s For Authors carefully before submission. Prospective authors should submit an electronic copy of their complete manuscript through the journal’s Paper Submission System.

Please kindly notice that the “Special Issue” under your manuscript title is supposed to be specified and the research field “Special Issue - Statistical Modeling and Computation” should be chosen during your submission.

According to the following timetable:

Guest Editor:

For further questions or inquiries

Please contact Editorial Assistant at

ojs@scirp.org