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Axioms
Special Issues
Statistical Signal Processing: Recent Advances
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Statistical Signal Processing: Recent Advances

A special issue of Axioms (ISSN 2075-1680).

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 1161

Special Issue Editors

Dr. Nizar Bouhlel

E-Mail Website
Guest Editor
Institut Agro, Univ Angers1, INRAE, IRHS, SFR QuaSaV, 49000 Angers, France
Interests: multivariate statistics; statistical image processing; statistical signal processing; remote sensing; SAR; SAR polarimetry
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Stéphane Méric

E-Mail Website
Guest Editor
Institut d'Electronique et des Technologies du numeRique (IETR - UMR CNRS 6164), Rennes, France
Interests: scattering; synthetic aperture radar; RADARSAT

Special Issue Information

Dear Colleagues,

We are pleased to announce the launch of a new Special Issue in Axioms, entitled “Statistical Signal Processing: Recent Advances”. With the random nature of signals, statistical methods play an important role in signal processing. Statistics are used in the formulation of adequate models to describe the behavior of systems, the development of adequate techniques for the estimation of model parameters and the evaluation of its performances. With the recent technological developments, we are increasingly confronted with complex systems that produce large and high-dimensional data, which require modern, efficient and fast statistical signal processing methods.

The purpose of this Special Issue is to explore new statistical signal processing methods in model formulations, parameter estimation and data analysis.
Topics of interest include, but are not limited to, the following:

  • Hierarchical Bayesian models and Bayesian inference,
  • Variational Bayesian model and variational Bayesian inference,
  • Approximate Bayesian computation (ABC),
  • Bayesian non-parametric models and non-parametric Bayesian inference,
  • The Dirichlet process and hierarchical Dirichlet process.

We look forward to your contributions.

Prof. Dr. Nizar Bouhlel
Prof. Dr. Stéphane Méric
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. Axioms 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 2400 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

  • statistical inference
  • Bayesian data analysis
  • Bayesian inference
  • model selection
  • high-dimensional data
  • analysis of parameter estimation
  • Bayesian non-parametric statistics

Published Papers (1 paper)

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Research

25 pages, 19007 KiB  
Article
Statistical Analysis and Applications of Adaptive Progressively Type-II Hybrid Poisson–Exponential Censored Data
by Ahmed Elshahhat and Heba S. Mohammed
Axioms 2023, 12(6), 533; https://doi.org/10.3390/axioms12060533 - 29 May 2023
Cited by 2 | Viewed by 803
Abstract
A new two-parameter extended exponential lifetime distribution with an increasing failure rate called the Poisson–exponential (PE) model was explored. In the reliability experiments, an adaptive progressively Type-II hybrid censoring strategy is presented to improve the statistical analysis efficiency and reduce the entire test [...] Read more.
A new two-parameter extended exponential lifetime distribution with an increasing failure rate called the Poisson–exponential (PE) model was explored. In the reliability experiments, an adaptive progressively Type-II hybrid censoring strategy is presented to improve the statistical analysis efficiency and reduce the entire test duration on a life-testing experiment. To benefit from this mechanism, this paper sought to infer the unknown parameters, as well as the reliability and failure rate function of the PE distribution using both the likelihood and product of spacings estimation procedures as a conventional view. For each unknown parameter, from both classical approaches, an approximate confidence interval based on Fisher’s information was also created. Additionally, in the Bayesian paradigm, the given classical approaches were extended to Bayes’ continuous theorem to develop the Bayes (or credible interval) estimates of the same unknown quantities. Employing the squared error loss, the Bayesian inference was developed based on independent gamma assumptions. Because of the complex nature of the posterior density, the Markov chain with the Monte Carlo methodology was used to obtain data from the whole conditional distributions and, therefore, evaluate the acquired Bayes point/interval estimates. Via extensive numerical comparisons, the performance of the estimates provided was evaluated with respect to various criteria. Among different competing progressive mechanisms, using four optimality criteria, the best censoring was suggested. Two real chemical engineering datasets were also analyzed to highlight the applicability of the acquired point and interval estimators in an actual practical scenario. Full article
(This article belongs to the Special Issue Statistical Signal Processing: Recent Advances)
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