Special Issue on Nonparametric Statistics for Big Data

 

Nonparametric statistics is a basic area of statistics, at the interface of data mining, mathematics, engineering, machine learning, computer science and biomedical research. Modern data is increasing very large in terms of both the number of objects and the number of dimensions. While in a statistical sense massive amounts of data make nonparametric methods entirely appropriate. It has been created for big data visualization, geometric representation, dimension reduction, modeling and inference.

 

In this special issue, we intend to invite front-line researchers and authors to submit original research and review articles on exploring nonparametric statistics for big data. Potential topics include, but are not limited to:

• Nonparametric regression
• Nonparametric methods
• Regularization and feature selection
• High-dimensional inference and theory
• Spatial and environmental statistics
• Image analysis
• Statistical machine learning
• Applications

 

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 specify the “Special Issue” under your manuscript title. The research field “Special Issue - Nonparametric Statistics for Big Data” should be selected during your submission.

 

Special Issue timetable:

 

 

Guest Editor:

Prof. Qihua Wang

Chinese Academy of Sciences, China

 

For further questions or inquiries

Please contact Editorial Assistant at

ojs@scirp.org