Special Issue "Modern Statistical Methods for Spatial and Multivariate Data"

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".

Deadline for manuscript submissions: 20 January 2022.

Special Issue Editor

Dr. Norou Diawara
E-Mail Website
Guest Editor
Department of Mathematics and Statistics, Old Dominion University, 4700 Elkhorn Ave, Norfolk, VA 23529, USA
Interests: spatio-temporal data; discrete choice models; multivariate statistics

Special Issue Information

Dear Colleagues,

Please contribute to this Special Issue on generalized count regression, and inference under spatial or temporal multivariate data, called “Statistical Methods for Multivariate Data”, for the open-access journal Computation (https://0-www-mdpi-com.brum.beds.ac.uk/journal/computation). We are looking at new paradigms and strategies in solving multivariate data problems under real, simulated and computational challenges.

The journal has also provided the opportunity to submit a review of the literature related to the Special Issue topic. If you would consider working on a review paper, that will also provide a mean for a strong contribution to the Special Issue overall. It will be an honor to have your name included among the list of contributing authors to this Special Issue.

Dr. Norou Diawara
Guest Editor

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 papers will be 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. Computation is an international peer-reviewed open access monthly 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 1400 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.

Keywords

  • Multivariate data
  • Spatio-temporal analysis methods
  • Simulations and computational statistics
  • Functional data analysis

Published Papers (3 papers)

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Research

Article
A Class of Copula-Based Bivariate Poisson Time Series Models with Applications
Computation 2021, 9(10), 108; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9100108 - 18 Oct 2021
Viewed by 370
Abstract
A class of bivariate integer-valued time series models was constructed via copula theory. Each series follows a Markov chain with the serial dependence captured using copula-based transition probabilities from the Poisson and the zero-inflated Poisson (ZIP) margins. The copula theory was also used [...] Read more.
A class of bivariate integer-valued time series models was constructed via copula theory. Each series follows a Markov chain with the serial dependence captured using copula-based transition probabilities from the Poisson and the zero-inflated Poisson (ZIP) margins. The copula theory was also used again to capture the dependence between the two series using either the bivariate Gaussian or “t-copula” functions. Such a method provides a flexible dependence structure that allows for positive and negative correlation, as well. In addition, the use of a copula permits applying different margins with a complicated structure such as the ZIP distribution. Likelihood-based inference was used to estimate the models’ parameters with the bivariate integrals of the Gaussian or t-copula functions being evaluated using standard randomized Monte Carlo methods. To evaluate the proposed class of models, a comprehensive simulated study was conducted. Then, two sets of real-life examples were analyzed assuming the Poisson and the ZIP marginals, respectively. The results showed the superiority of the proposed class of models. Full article
(This article belongs to the Special Issue Modern Statistical Methods for Spatial and Multivariate Data)
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Article
Novel Statistical Analysis in the Context of a Comprehensive Needs Assessment for Secondary STEM Recruitment
Computation 2021, 9(10), 105; https://doi.org/10.3390/computation9100105 - 28 Sep 2021
Viewed by 300
Abstract
There is a myriad of career opportunities stemming from science, technology, engineering, and mathematics (STEM) disciplines. In addition to careers in corporate settings, teaching is a viable career option for individuals pursuing degrees in STEM disciplines. With national shortages of secondary STEM teachers, [...] Read more.
There is a myriad of career opportunities stemming from science, technology, engineering, and mathematics (STEM) disciplines. In addition to careers in corporate settings, teaching is a viable career option for individuals pursuing degrees in STEM disciplines. With national shortages of secondary STEM teachers, efforts to recruit, train, and retain quality STEM teachers is greatly important. Prior to exploring ways to attract potential STEM teacher candidates to pursue teacher training programs, it is important to understand the perceived value that potential recruits place on STEM careers, disciplines, and the teaching profession. The purpose of this study was to explore students’ perceptions of the usefulness of STEM disciplines and their value in supporting students’ careers. A novel statistical method was utilized, combining exploratory-factor analysis, the analysis of variance, generalized estimating equation evaluations under the framework of a generalized linear model, and quantile regression. Using the outputs from each statistical measure, students’ valuation of each STEM discipline and their interest in pursuing teaching as a career option were assessed. Our results indicate a high correlation of liking and perceived usability of the STE disciplines relative to careers. Conversely, our results also display a low correlation of the liking and perceived usability of mathematics relative to future careers. The significance of these diametrically related results suggests the need for promotion of the interrelatedness of mathematics and STE. Full article
(This article belongs to the Special Issue Modern Statistical Methods for Spatial and Multivariate Data)
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Article
EM Estimation for Zero- and k-Inflated Poisson Regression Model
Computation 2021, 9(9), 94; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9090094 - 26 Aug 2021
Viewed by 447
Abstract
Count data with excessive zeros are ubiquitous in healthcare, medical, and scientific studies. There are numerous articles that show how to fit Poisson and other models which account for the excessive zeros. However, in many situations, besides zero, the frequency of another count [...] Read more.
Count data with excessive zeros are ubiquitous in healthcare, medical, and scientific studies. There are numerous articles that show how to fit Poisson and other models which account for the excessive zeros. However, in many situations, besides zero, the frequency of another count k tends to be higher in the data. The zero- and k-inflated Poisson distribution model (ZkIP) is appropriate in such situations The ZkIP distribution essentially is a mixture distribution of Poisson and degenerate distributions at points zero and k. In this article, we study the fundamental properties of this mixture distribution. Using stochastic representation, we provide details for obtaining parameter estimates of the ZkIP regression model using the Expectation–Maximization (EM) algorithm for a given data. We derive the standard errors of the EM estimates by computing the complete, missing, and observed data information matrices. We present the analysis of two real-life data using the methods outlined in the paper. Full article
(This article belongs to the Special Issue Modern Statistical Methods for Spatial and Multivariate Data)
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