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Computational Statistics and Data Analysis, 2nd Edition

This special issue belongs to the section “D1: Probability and Statistics“.

Special Issue Information

Dear Colleagues, 

With the development of scientific techniques, computational statistics and data analysis have become more and more important in diverse areas of science, engineering, and humanities, ranging from genomics and health sciences to economics, finance, and machine learning. To analyze the real data in these fields, statistical methodologies and computing for data analysis are fundamental to statistical modeling and data analysis. In this Special Issue, we are looking for high-quality research papers in computational statistics and data analysis. We invite investigators to contribute original research articles as well as review articles that will stimulate the development of statistical methodology and applications concerning the data analysis.

Prof. Dr. Gaorong Li
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • bootstrapping
  • classification
  • data analytical strategies and methodologies applied in biostatistics
  • dimension reduction of high-dimensional data analysis
  • large-scale inference for Gaussian graphical models and covariance estimation
  • longitudinal/panel data analysis
  • massive networks
  • medical statistics
  • nonparametric and semiparametric models
  • optimal portfolio
  • robust statistics
  • statistical methodology and computing for data analysis
  • statistical methodology and computing for noise data, such as measurement error data, missing data etc.
  • sufficient dimension reduction methods in regression analysis variable/model selection for high-dimensional data

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Mathematics - ISSN 2227-7390
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