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Directions in Statistical Modelling
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Directions in Statistical Modelling

A special issue of Stats (ISSN 2571-905X).

Deadline for manuscript submissions: closed (15 June 2021) | Viewed by 8084

Special Issue Editor

Prof. Dr. Célestin C. Kokonendji

E-Mail Website
Guest Editor
Laboratoire de Mathématiques de Besançon UMR 6623 CNRS-UFC, Université Bourgogne Franche-Comté, UFR Sciences et Techniques, 16 route de Gray, 25030 Besançon CEDEX, France
Interests: mathematical statistics; associate kernel methods (discrete/continuous/mixed); nonparametric and semiparametric estimation; Bayesian analysis; multivariate count distributions; models and analysis of over/under-dispersed count data; relative variability indexes; non-negative orthant distributions; environmental statistics; statistical inference; distribution theory; characterizations; (exponential/geometric/factorial/discrete) dispersion models; generalized variance functions; Monge–Ampère equation; pseudo-orthogonal polynomials; stochastic/Lévy processes; applied stochastic models

Special Issue Information

Dear Colleagues,

It is my pleasure to announce the launch of a new Special Issue in Stats on “Directions in Statistical Modelling”. We request your contributions toward a collection of papers, including research articles, reviews, communications, and concept papers, pertaining to a topic relevant to the analysis and modelling of uni/multivariate nonnegative orthant (count and (semi)continuous) data.

Frameworks of time series, generalized linear/mixed models, space data models, Bayesian and Markovian approaches, smoothing and asymmetric kernel methods, semiparametric models, and weighting models are mainly required. Our community needs to discuss the latest research and develop new ideas and research directions.

An expression of interest with an approximately 200-word abstract should be sent to the Guest Editor or the editorial office prior to submission. Five high-quality papers will be considered for publication free of charge in this Special Issue, which will be selected on the basis of high quality.

Prof. Dr. Célestin C. Kokonendji
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. Stats is an international peer-reviewed open access quarterly 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 1600 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

  • Bayesian modelling
  • Generalized mixed models
  • Markovian modelling
  • Nonnegative orthant distributions
  • Semiparametric models
  • Smoothing and asymmetric kernel
  • Space data models
  • Time series
  • Weightening models

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Published Papers (2 papers)

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Research

19 pages, 389 KiB  
Article
A Flexible Multivariate Distribution for Correlated Count Data
by Kimberly F. Sellers, Tong Li, Yixuan Wu and Narayanaswamy Balakrishnan
Stats 2021, 4(2), 308-326; https://doi.org/10.3390/stats4020021 - 15 Apr 2021
Cited by 6 | Viewed by 4566
Abstract
Multivariate count data are often modeled via a multivariate Poisson distribution, but it contains an underlying, constraining assumption of data equi-dispersion (where its variance equals its mean). Real data are oftentimes over-dispersed and, as such, consider various advancements of a negative binomial structure. [...] Read more.
Multivariate count data are often modeled via a multivariate Poisson distribution, but it contains an underlying, constraining assumption of data equi-dispersion (where its variance equals its mean). Real data are oftentimes over-dispersed and, as such, consider various advancements of a negative binomial structure. While data over-dispersion is more prevalent than under-dispersion in real data, however, examples containing under-dispersed data are surfacing with greater frequency. Thus, there is a demonstrated need for a flexible model that can accommodate both data types. We develop a multivariate Conway–Maxwell–Poisson (MCMP) distribution to serve as a flexible alternative for correlated count data that contain data dispersion. This structure contains the multivariate Poisson, multivariate geometric, and the multivariate Bernoulli distributions as special cases, and serves as a bridge distribution across these three classical models to address other levels of over- or under-dispersion. In this work, we not only derive the distributional form and statistical properties of this model, but we further address parameter estimation, establish informative hypothesis tests to detect statistically significant data dispersion and aid in model parsimony, and illustrate the distribution’s flexibility through several simulated and real-world data examples. These examples demonstrate that the MCMP distribution performs on par with the multivariate negative binomial distribution for over-dispersed data, and proves particularly beneficial in effectively representing under-dispersed data. Thus, the MCMP distribution offers an effective, unifying framework for modeling over- or under-dispersed multivariate correlated count data that do not necessarily adhere to Poisson assumptions. Full article
(This article belongs to the Special Issue Directions in Statistical Modelling)
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22 pages, 509 KiB  
Article
Bayesian Bandwidths in Semiparametric Modelling for Nonnegative Orthant Data with Diagnostics
by Célestin C. Kokonendji and Sobom M. Somé
Stats 2021, 4(1), 162-183; https://doi.org/10.3390/stats4010013 - 4 Mar 2021
Cited by 11 | Viewed by 3104
Abstract
Multivariate nonnegative orthant data are real vectors bounded to the left by the null vector, and they can be continuous, discrete or mixed. We first review the recent relative variability indexes for multivariate nonnegative continuous and count distributions. As a prelude, the classification [...] Read more.
Multivariate nonnegative orthant data are real vectors bounded to the left by the null vector, and they can be continuous, discrete or mixed. We first review the recent relative variability indexes for multivariate nonnegative continuous and count distributions. As a prelude, the classification of two comparable distributions having the same mean vector is done through under-, equi- and over-variability with respect to the reference distribution. Multivariate associated kernel estimators are then reviewed with new proposals that can accommodate any nonnegative orthant dataset. We focus on bandwidth matrix selections by adaptive and local Bayesian methods for semicontinuous and counting supports, respectively. We finally introduce a flexible semiparametric approach for estimating all these distributions on nonnegative supports. The corresponding estimator is directed by a given parametric part, and a nonparametric part which is a weight function to be estimated through multivariate associated kernels. A diagnostic model is also discussed to make an appropriate choice between the parametric, semiparametric and nonparametric approaches. The retention of pure nonparametric means the inconvenience of parametric part used in the modelization. Multivariate real data examples in semicontinuous setup as reliability are gradually considered to illustrate the proposed approach. Concluding remarks are made for extension to other multiple functions. Full article
(This article belongs to the Special Issue Directions in Statistical Modelling)
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