Application in Computational Statistics and Data Science
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".
Deadline for manuscript submissions: 31 January 2025 | Viewed by 213
Special Issue Editor
Special Issue Information
Dear Colleagues,
Computational statistics designates efficient methods to obtain numerical solutions to statistically formulated problems. In this special edition, we will present a variety of computationally intensive statistical techniques with applications to real data. The topics include numerical optimization in statistical inference, generation of random numbers, Monte Carlo methods, randomization methods, jackknife methods, bootstrap methods and extensions of regression models in general.
On the other hand, data science is a multidisciplinary field of science that involves computation techniques, applied mathematics, artificial intelligence (machine learning, neural networks), statistics and optimization, with the aim of analytically resolving complex problems, employing large datasets as the core of operations.
This Special Issue is being planned to promote research into modern and sophisticated computationally intensive statistical techniques and data science methods with varied applications.
Prof. Dr. Edwin Moises Marcos Ortega
Guest Editor
Manuscript Submission Information
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Keywords
- computational statistics
- data science
- machine learning
- regression models
- statistical inference
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