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Mathematics
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Advances in Statistical Computing
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Advances in Statistical Computing

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".

Deadline for manuscript submissions: closed (1 August 2023) | Viewed by 12228

Special Issue Editors

Dr. Philip D. Waggoner

E-Mail Website
Guest Editor
Institute for Social and Economic Research and Policy, Columbia University and YouGov America, New York, NY 10027, USA
Interests: data science; machine learning; statistical computing; computational social science
Dr. Dominique Makowski

E-Mail Website
Guest Editor
The Clinical Brain Lab, Nanyang Technological University, Singapore, Singapore
Interests: sense of reality; emotion regulation; deception; cognitive control; neuropsychology

Special Issue Information

Dear Colleagues,

We are excited to announce a call for papers for a Special Issue in the journal Mathematics, “Advances in Statistical Computing.” As the name of the Special Issue suggests, the goal is to showcase exciting new developments in the field of statistical computing. These developments, from new algorithms and methods to a deep dive into interdisciplinary team-based development or normative discussions on open science, need not be exclusively “mathematical” in nature. That is, though authors may certainly rely heavily on mathematics if desired, the goal of this Special Issue is more broadly focused on fostering a discussion surrounding the rapid and exciting development of the field of statistical computing, broadly defined. Such an effort will necessarily include a range of submissions such as high-level discussions pushing the field to consider significant challenges and opportunities, technical papers that introduce and unpack new methods, or even a reimagining of an existing paradigm. It is our hope that a wide selection of papers will contribute to a thorough treatment of where we are as a field, and the boundless potential of where we are headed. Of note, the development and advancement of statistical computing is occurring everywhere, both in and out of academia. As such, scholars at all stages from graduate students to faculty members and those in industry are encouraged to submit to this Special Issue to incorporate as many voices and perspectives in this critical topic as possible.

Dr. Philip D. Waggoner
Dr. Dominique Makowski
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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Editorial

Jump to: Research

9 pages, 230 KiB  
Editorial
Where Are We Going with Statistical Computing? From Mathematical Statistics to Collaborative Data Science
by Dominique Makowski and Philip D. Waggoner
Mathematics 2023, 11(8), 1821; https://doi.org/10.3390/math11081821 - 12 Apr 2023
Cited by 1 | Viewed by 1890
Abstract
The field of statistical computing is rapidly developing and evolving. Shifting away from the formerly siloed landscape of mathematics, statistics, and computer science, recent advancements in statistical computing are largely characterized by a fusing of these worlds; namely, programming, software development, and applied [...] Read more.
The field of statistical computing is rapidly developing and evolving. Shifting away from the formerly siloed landscape of mathematics, statistics, and computer science, recent advancements in statistical computing are largely characterized by a fusing of these worlds; namely, programming, software development, and applied statistics are merging in new and exciting ways. There are numerous drivers behind this advancement, including open movement (encompassing development, science, and access), the advent of data science as a field, and collaborative problem-solving, as well as practice-altering advances in subfields such as artificial intelligence, machine learning, and Bayesian estimation. In this paper, we trace this shift in how modern statistical computing is performed, and that which has recently emerged from it. This discussion points to a future of boundless potential for the field. Full article
(This article belongs to the Special Issue Advances in Statistical Computing)

Research

Jump to: Editorial

10 pages, 1766 KiB  
Article
Phi, Fei, Fo, Fum: Effect Sizes for Categorical Data That Use the Chi-Squared Statistic
by Mattan S. Ben-Shachar, Indrajeet Patil, Rémi Thériault, Brenton M. Wiernik and Daniel Lüdecke
Mathematics 2023, 11(9), 1982; https://doi.org/10.3390/math11091982 - 22 Apr 2023
Cited by 10 | Viewed by 6117
Abstract
In both theoretical and applied research, it is often of interest to assess the strength of an observed association. Existing guidelines also frequently recommend going beyond null-hypothesis significance testing and reporting effect sizes and their confidence intervals. As such, measures of effect sizes [...] Read more.
In both theoretical and applied research, it is often of interest to assess the strength of an observed association. Existing guidelines also frequently recommend going beyond null-hypothesis significance testing and reporting effect sizes and their confidence intervals. As such, measures of effect sizes are increasingly reported, valued, and understood. Beyond their value in shaping the interpretation of the results from a given study, reporting effect sizes is critical for meta-analyses, which rely on their aggregation. We review the most common effect sizes for analyses of categorical variables that use the χ2 (chi-square) statistic and introduce a new effect size—פ (Fei, pronounced “fay”). We demonstrate the implementation of these measures and their confidence intervals via the effectsize package in the R programming language. Full article
(This article belongs to the Special Issue Advances in Statistical Computing)
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23 pages, 1857 KiB  
Article
Applied Geospatial Bayesian Modeling in the Big Data Era: Challenges and Solutions
by Jason S. Byers and Jeff Gill
Mathematics 2022, 10(21), 4116; https://doi.org/10.3390/math10214116 - 4 Nov 2022
Cited by 2 | Viewed by 1636
Abstract
Two important trends in applied statistics are an increased usage of geospatial models and an increased usage of big data. Naturally, there has been overlap as analysts utilize the techniques associated with each. With geospatial methods such as kriging, the computation required becomes [...] Read more.
Two important trends in applied statistics are an increased usage of geospatial models and an increased usage of big data. Naturally, there has been overlap as analysts utilize the techniques associated with each. With geospatial methods such as kriging, the computation required becomes intensive quickly, even with datasets that would not be considered huge in other contexts. In this work we describe a solution to the computational problem of estimating Bayesian kriging models with big data, Bootstrap Random Spatial Sampling (BRSS), and first provide an analytical argument that BRSS produces consistent estimates from the Bayesian spatial model. Second, with a medium-sized dataset on fracking in West Virginia, we show that bootstrap sample effects from a full-information Bayesian model are reduced with more bootstrap samples and more observations per sample as in standard bootstrapping. Third, we offer a realistic illustration of the method by analyzing campaign donors in California with a large geocoded dataset. With this solution, scholars will not be constrained in their ability to apply theoretically relevant geospatial Bayesian models when the size of the data produces computational intractability. Full article
(This article belongs to the Special Issue Advances in Statistical Computing)
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13 pages, 1254 KiB  
Article
Modified Class of Estimators Using Ranked Set Sampling
by Shashi Bhushan, Anoop Kumar, Sana Shahab, Showkat Ahmad Lone and Salemah A. Almutlak
Mathematics 2022, 10(21), 3921; https://doi.org/10.3390/math10213921 - 22 Oct 2022
Cited by 4 | Viewed by 1253
Abstract
The present article discusses the issue of population mean estimation in the ranked set sampling framework. A modified class of estimators is proffered and compared in the aspect of its efficacious performance with all salient conventional estimators existing to date. Some well-known existing [...] Read more.
The present article discusses the issue of population mean estimation in the ranked set sampling framework. A modified class of estimators is proffered and compared in the aspect of its efficacious performance with all salient conventional estimators existing to date. Some well-known existing estimators under RSS are recognized as the members of the proffered estimators for appropriately chosen characterizing scalars. The ascendancy of the proposed class of estimators regarding the conventional estimators has been shown through an extensive computational study utilizing some natural and artificially generated populations. Full article
(This article belongs to the Special Issue Advances in Statistical Computing)
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