Special Issue "Applied and Computational Statistics"

A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: closed (30 April 2019).

Special Issue Editor

Prof. Dr. Sorana D. Bolboacă
E-Mail Website
Guest Editor
Department of Medical Informatics and Biostatistics, Iuliu Haţieganu University of Medicine and Pharmacy, Louis Pasteur Str., No. 6, 400349 Cluj-Napoca, Cluj, Romania
Interests: Applied & Computational Statistics; Molecular Modeling; Genetic Analysis; Statistical Modeling in Medicine; Integrated Health Informatics System; Medical Diagnostic Research; Statistical Inference; Medical Imaging Analysis; Assisted Decision Systems; Research Ethics; Social Media and Health Information; Evidence-Based Medicine
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Special Issue Information

Dear Colleagues,

The researches on statistical population, sample, or model have application in all research areas and are conducted to gain knowledge for real-world problems. The increase of the power of calculation opens the path to computational statistics, translation of algorithms, implementation of statistical methods and computer simulations. These areas are developing rapidly, providing solutions to multidisciplinary, interdisciplinary, or transdisciplinary topics. The goal of this Special Issue is to present a collection of articles on applied and computational statistical methods, methodologies, applications and software, a bridge between statistic theory and its applications.

Submissions in areas of applied and computational statistics are invited, reporting (but not limited to):

  • Methods (including resampling, optimization, Monte Carlo, regression, artificial neural networks, etc.)
  • Algorithms (including design, analysis, and validation) for numerical analysis, statistics, image processing, etc.
  • Software (including different implementations, software validation, software errors, etc.)
  • Data analysis (including inferential statistics, distribution theory, stochastic analysis, etc.)
  • Big data (analysis, management, retrieval, etc.)
  • Data mining and data science (databases, visualization, etc.)
  • In silico approaches, methodologies and results (including with parallel and distributed computing)

Are welcomed high-quality original theoretical and applied research papers, reviews, articles crossing biological and medical sciences, computer science and information technology, engineering, social sciences, etc.

Prof. Dr. Sorana D. Bolboaca
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. Mathematics 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 1200 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 and computational methods
  • Statistical and computational methodologies and approaches
  • Statistics algorithms and software
  • In silico statistical approaches
  • Data analysis and analytics
  • Big data and data mining

Published Papers (5 papers)

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Research

Open AccessArticle
A Model for Predicting Statement Mutation Scores
Mathematics 2019, 7(9), 778; https://doi.org/10.3390/math7090778 - 23 Aug 2019
Abstract
A test suite plays a key role in software testing. Mutation testing is a powerful approach to measure the fault-detection ability of a test suite. The mutation testing process requires a large number of mutants to be generated and executed. Hence, mutation testing [...] Read more.
A test suite plays a key role in software testing. Mutation testing is a powerful approach to measure the fault-detection ability of a test suite. The mutation testing process requires a large number of mutants to be generated and executed. Hence, mutation testing is also computationally expensive. To solve this problem, predictive mutation testing builds a classification model to predict the test result of each mutant. However, the existing predictive mutation testing methods only can be used to estimate the overall mutation scores of object-oriented programs. To overcome the shortcomings of the existing methods, we propose a new method to directly predict the mutation score for each statement in process-oriented programs. Compared with the existing predictive mutation testing methods, our method uses more dynamic program execution features, which more adequately reflect dynamic dependency relationships among the statements and more accurately reflects information propagation during the execution of test cases. By comparing the prediction effects of logistic regression, artificial neural network, random forest, support vector machine, and symbolic regression, we finally decide to use a single hidden layer feedforward neural network as the predictive model to predict the statement mutation scores. In our two experiments, the mean absolute errors between the statement mutation scores predicted by the neural network and the real statement mutation scores both approximately reach 0.12. Full article
(This article belongs to the Special Issue Applied and Computational Statistics)
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Open AccessArticle
Optimal Repeated Measurements for Two Treatment Designs with Dependent Observations: The Case of Compound Symmetry
Mathematics 2019, 7(4), 378; https://doi.org/10.3390/math7040378 - 25 Apr 2019
Abstract
In this paper, we construct optimal repeated measurement designs of two treatments for estimating direct effects, and we examine the case of compound symmetry dependency. We present the model and the design that minimizes the variance of the estimated difference of the two [...] Read more.
In this paper, we construct optimal repeated measurement designs of two treatments for estimating direct effects, and we examine the case of compound symmetry dependency. We present the model and the design that minimizes the variance of the estimated difference of the two treatments. The optimal designs with dependent observations in a compound symmetry model are the same as in the case of independent observations. Full article
(This article belongs to the Special Issue Applied and Computational Statistics)
Open AccessArticle
The Modified Beta Gompertz Distribution: Theory and Applications
Mathematics 2019, 7(1), 3; https://doi.org/10.3390/math7010003 - 20 Dec 2018
Abstract
In this paper, we introduce a new continuous probability distribution with five parameters called the modified beta Gompertz distribution. It is derived from the modified beta generator proposed by Nadarajah, Teimouri and Shih (2014) and the Gompertz distribution. By investigating its mathematical and [...] Read more.
In this paper, we introduce a new continuous probability distribution with five parameters called the modified beta Gompertz distribution. It is derived from the modified beta generator proposed by Nadarajah, Teimouri and Shih (2014) and the Gompertz distribution. By investigating its mathematical and practical aspects, we prove that it is quite flexible and can be used effectively in modeling a wide variety of real phenomena. Among others, we provide useful expansions of crucial functions, quantile function, moments, incomplete moments, moment generating function, entropies and order statistics. We explore the estimation of the model parameters by the obtained maximum likelihood method. We also present a simulation study testing the validity of maximum likelihood estimators. Finally, we illustrate the flexibility of the distribution by the consideration of two real datasets. Full article
(This article belongs to the Special Issue Applied and Computational Statistics)
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Open AccessArticle
Computation of Probability Associated with Anderson–Darling Statistic
Mathematics 2018, 6(6), 88; https://doi.org/10.3390/math6060088 - 25 May 2018
Cited by 5
Abstract
The correct application of a statistical test is directly connected with information related to the distribution of data. Anderson–Darling is one alternative used to test if the distribution of experimental data follows a theoretical distribution. The conclusion of the Anderson–Darling test is usually [...] Read more.
The correct application of a statistical test is directly connected with information related to the distribution of data. Anderson–Darling is one alternative used to test if the distribution of experimental data follows a theoretical distribution. The conclusion of the Anderson–Darling test is usually drawn by comparing the obtained statistic with the available critical value, which did not give any weight to the same size. This study aimed to provide a formula for calculation of p-value associated with the Anderson–Darling statistic considering the size of the sample. A Monte Carlo simulation study was conducted for sample sizes starting from 2 to 61, and based on the obtained results, a formula able to give reliable probabilities associated to the Anderson–Darling statistic is reported. Full article
(This article belongs to the Special Issue Applied and Computational Statistics)
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Open AccessFeature PaperArticle
Extending the Characteristic Polynomial for Characterization of C20 Fullerene Congeners
Mathematics 2017, 5(4), 84; https://doi.org/10.3390/math5040084 - 19 Dec 2017
Cited by 4
Abstract
The characteristic polynomial (ChP) has found its use in the characterization of chemical compounds since Hückel’s method of molecular orbitals. In order to discriminate the atoms of different elements and different bonds, an extension of the classical definition is required. The extending characteristic [...] Read more.
The characteristic polynomial (ChP) has found its use in the characterization of chemical compounds since Hückel’s method of molecular orbitals. In order to discriminate the atoms of different elements and different bonds, an extension of the classical definition is required. The extending characteristic polynomial (EChP) family of structural descriptors is introduced in this article. Distinguishable atoms and bonds in the context of chemical structures are considered in the creation of the family of descriptors. The extension finds its uses in problems requiring discrimination among same-patterned graph representations of molecules as well as in problems involving relations between the structure and the properties of chemical compounds. The ability of the EChP to explain two properties, namely, area and volume, is analyzed on a sample of C20 fullerene congeners. The results have shown that the EChP-selected descriptors well explain the properties. Full article
(This article belongs to the Special Issue Applied and Computational Statistics)
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