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
|
September 30th,
2022
|
Publication Date
|
October 2022
|
Guest Editor:
For
further questions or inquiries
Please
contact Editorial Assistant at
ojs@scirp.org