Special Issue on Statistical Modeling and Analysis
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 analysis.
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 analysis. 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 Analysis” should be
chosen during your submission.
According
to the following timetable:
Submission Deadline
|
November 15th, 2020
|
Publication Date
|
February 2021
|
Guest Editor:
For further
questions or inquiries
Please contact
Editorial Assistant at
ojs@scirp.org