SI AMiDA 2022 : Special Issue on Advanced Methods in Data Analysis
Call For Papers
SPECIAL ISSUE ON ADVANCED METHODS IN DATA ANALYSIS
Christophe Chesneau, University of Caen, France
The deadline for submissions is MARCH 1, 2022, but individual papers will be reviewed and published online on an ongoing basis.
This thematic special issue in Open Statistics is devoted to the advanced methods in data analysis.
In recent years, many new techniques have been developed in the literature, revisiting traditional approaches thanks to technological advancements. Regression, machine learning, nonparametric estimation, statistical algorithms, Bayes methods,
and sampling techniques are all examples. The goal of this special issue is to compile a collection of original articles that apply these techniques to current data in a variety of fields (biology, medicine, agriculture, engineering, hydrology, finance, and so on). The importance of the analysis’ discussion, interpretations, and consequences for the future generation will be crucial.
Contributions to the Special Issue may address (but are not limited) to the following aspects:
• Contemporary data analysis
• Nonparametric estimation
• Statistical algorithm
• Machine learning
• Artificial intelligence
• Data mining
Authors are requested to submit their full revised papers complying with the general scope of the journal. The submitted papers will undergo the standard peer-review process before they can be accepted. Notification of acceptance will be communicated
as we progress with the review process.
ABOUT THE JOURNAL:
Open Statistics is a fully peer-reviewed, open access, electronic journal that publishes significant, original, and relevant works in all areas of statistics.
For more information, please visit our website
CALL FOR PAPERS:
HOW TO SUBMIT
Manuscripts should be submitted to the journal via online submission system Editorial Manager available for this journal
In case of questions, please contact the Editorial Office of this journal at firstname.lastname@example.org