Advances in Computational Statistics and Data Analysis

Dear Colleagues,

Recent advancements in computer technology, paired with the ever-improving accessibility and affordability of computing resources, have revolutionized the way we perform data analysis. Rooted in scientific computing and located at the interface between statistics and computer science, computational statistics offers powerful tools—readily available to both researchers and practitioners—indispensable for analyzing constantly growing amounts of data of increasing complexity.

Presently, computer-intensive statistical methodologies are broadly used to analyze large and complex real-world datasets arising in engineering, business, finance, cybersecurity, medicine and healthcare, biology, ecology, etc. Typical problems range from classical estimation and inference questions to unsupervised, supervised, and reinforcement learning tasks. Instead of relying on restrictive parametric assumptions, large-sample approaches, or other theoretical simplifications, computational statistics harnesses the power of modern computing-intensive techniques such as resampling and bootstrap; numerical optimization and equation solving; parallel, distributed, and cloud computing; and advanced nonparametric techniques, including tree-based learning and neural networks, advanced visualization instruments, etc.

The present Special Issue welcomes original manuscripts on a broad variety of topics in computational statistics and data analysis including, but not limited to: machine learning and statistical methodologies for advanced data analytics, theoretical aspects of computational statistics and data analysis, computational aspects of statistical analysis and machine learning, statistical software and algorithms, large-scale simulation experiments, application of advanced data analytics to real-world complex datasets, etc.

Dr. Su Chen
Dr. Michael Pokojovy
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• computer-intensive statistical methods
• advanced data analytics
• big data
• Bayesian computing
• robust estimation and inference
• nonparametric and semiparametric techniques
• computational finance
• econometrics
• biometrics
• biostatistics
• chemometrics
• statistical process control