Special Issue "Data Science: Measuring Uncertainties"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: 29 February 2020.

Special Issue Editors

Prof. Carlos Alberto De Bragança Pereira
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Guest Editor
Federal University of Mato Grosso do Sul, Campo Grande, MS, Brazil and University of Sao Paulo, Sao Paulo, SP, Brazil
Interests: Bayesian statistics; controversies and paradoxes in probability and statistics; Bayesian reliability; Bayesian analysis of discrete data (BADD); applied statistics
Special Issues and Collections in MDPI journals
Assoc. Prof. Adriano Polpo
E-Mail Website
Guest Editor
University of Western Australia, Crawley WA 6009, Australia
Interests: Bayesian inference; foundations of statistics; significance tests; reliability and survival analysis; model selection; biostatistics
Special Issues and Collections in MDPI journals
Assist. Prof. Agatha Rodrigues
E-Mail Website
Guest Editor
Federal University of Espirito Santo, ‎Vitória, ES, Brazil
Interests: data analysis; statistical analysis; statistical modeling; applied statistics; R statistical package
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The demand for data analysis is increasing day by day, and this is reflected in a large number of jobs and the high number of published articles. New solutions to the problems seem to be reproducing at a massive rate. A new era is coming! The dazzle is so great that many of us do not bother to check the suitability of the solutions for the problems that they are intended to solve. Current and future challenges require greater care in the creation of new solutions satisfying the rationality of each type of problem. Labels such as big data, data science, machine learning, statistical learning, and artificial intelligence are demanding more sophistication in the fundamentals and in the way that they are being applied.

This Special Issue is dedicated to solutions for and discussions of measuring uncertainties in data analysis problems. For example, considering the large amount of data related to an IoT (internet of things) problem, or even considering the small sample size of a biological study with huge dimensions, one must show how to properly understand the data, how to develop the best process of analysis and, finally, to illustrate how to apply the solutions that were obtained theoretically. We seek to respond to these challenges and publish papers that consider the reasons for a solution and how to apply them. Papers can cover existing methodologies by elucidating questions related to the reasons for their selection and their uses.

We are open to innovative solutions and theoretical works that justify the use of a method and to applied works that describe a good implementation of a theoretical method.

Prof. Carlos Alberto De Bragança Pereira
Assoc. Prof. Adriano Polpo
Assist. Prof. Agatha Rodrigues
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 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. Entropy 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 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.

Published Papers (2 papers)

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Research

Open AccessArticle
Universal Sample Size Invariant Measures for Uncertainty Quantification in Density Estimation
Entropy 2019, 21(11), 1120; https://doi.org/10.3390/e21111120 - 15 Nov 2019
Abstract
Previously, we developed a high throughput non-parametric maximum entropy method (PLOS ONE, 13(5): e0196937, 2018) that employs a log-likelihood scoring function to characterize uncertainty in trial probability density estimates through a scaled quantile residual (SQR). The SQR for the true probability density has [...] Read more.
Previously, we developed a high throughput non-parametric maximum entropy method (PLOS ONE, 13(5): e0196937, 2018) that employs a log-likelihood scoring function to characterize uncertainty in trial probability density estimates through a scaled quantile residual (SQR). The SQR for the true probability density has universal sample size invariant properties equivalent to sampled uniform random data (SURD). Alternative scoring functions are considered that include the Anderson-Darling test. Scoring function effectiveness is evaluated using receiver operator characteristics to quantify efficacy in discriminating SURD from decoy-SURD, and by comparing overall performance characteristics during density estimation across a diverse test set of known probability distributions. Full article
(This article belongs to the Special Issue Data Science: Measuring Uncertainties)
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Open AccessArticle
An Integrated Approach for Making Inference on the Number of Clusters in a Mixture Model
Entropy 2019, 21(11), 1063; https://doi.org/10.3390/e21111063 - 30 Oct 2019
Abstract
This paper presents an integrated approach for the estimation of the parameters of a mixture model in the context of data clustering. The method is designed to estimate the unknown number of clusters from observed data. For this, we marginalize out the weights [...] Read more.
This paper presents an integrated approach for the estimation of the parameters of a mixture model in the context of data clustering. The method is designed to estimate the unknown number of clusters from observed data. For this, we marginalize out the weights for getting allocation probabilities that depend on the number of clusters but not on the number of components of the mixture model. As an alternative to the stochastic expectation maximization (SEM) algorithm, we propose the integrated stochastic expectation maximization (ISEM) algorithm, which in contrast to SEM, does not need the specification, a priori, of the number of components of the mixture. Using this algorithm, one estimates the parameters associated with the clusters, with at least two observations, via local maximization of the likelihood function. In addition, at each iteration of the algorithm, there exists a positive probability of a new cluster being created by a single observation. Using simulated datasets, we compare the performance of the ISEM algorithm against both SEM and reversible jump (RJ) algorithms. The obtained results show that ISEM outperforms SEM and RJ algorithms. We also provide the performance of the three algorithms in two real datasets. Full article
(This article belongs to the Special Issue Data Science: Measuring Uncertainties)
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