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Computation
Special Issues
Modern Statistical Methods for Spatial and Multivariate Data
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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: closed (20 January 2022) | Viewed by 5256

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://www.mdpi.com/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 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. 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 1800 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 (2 papers)

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Research

Jump to: Review

16 pages, 338 KiB  
Article
EM Estimation for Zero- and k-Inflated Poisson Regression Model
by Monika Arora and N. Rao Chaganty
Computation 2021, 9(9), 94; https://doi.org/10.3390/computation9090094 - 26 Aug 2021
Cited by 4 | Viewed by 2840
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)

Review

Jump to: Research

9 pages, 322 KiB  
Review
More on the Supremum Statistic to Test Multivariate Skew-Normality
by Timothy Opheim and Anuradha Roy
Computation 2021, 9(12), 126; https://doi.org/10.3390/computation9120126 - 29 Nov 2021
Cited by 3 | Viewed by 1789
Abstract
This review is about verifying and generalizing the supremum test statistic developed by Balakrishnan et al. Exhaustive simulation studies are conducted for various dimensions to determine the effect, in terms of empirical size, of the supremum test statistic developed by Balakrishnan et al. [...] Read more.
This review is about verifying and generalizing the supremum test statistic developed by Balakrishnan et al. Exhaustive simulation studies are conducted for various dimensions to determine the effect, in terms of empirical size, of the supremum test statistic developed by Balakrishnan et al. to test multivariate skew-normality. Monte Carlo simulation studies indicate that the Type-I error of the supremum test can be controlled reasonably well for various dimensions for given nominal significance levels 0.05 and 0.01. Cut-off values are provided for the number of samples required to attain the nominal significance levels 0.05 and 0.01. Some new and relevant information of the supremum test statistic are reported here. Full article
(This article belongs to the Special Issue Modern Statistical Methods for Spatial and Multivariate Data)
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