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Mathematics
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Stochastic Modelling and Applied Probability in Climatology and Medicine
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Stochastic Modelling and Applied Probability in Climatology and Medicine

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".

Deadline for manuscript submissions: closed (10 March 2024) | Viewed by 4582

Special Issue Editor

Prof. Dr. Gerardo Sanz

E-Mail Website
Guest Editor
Dpto. Métodos Estadísticos and BIFI, Facultad de Ciencias, Universidad de Zaragoza, 50009 Zaragoza, Spain
Interests: statistical analysis; probability; mathematical statistics; statistics; stochastic processes; stochastic modeling; applied probability; stochastic analysis; Markov processes; probability theory

Special Issue Information

Dear Colleagues,

I am pleased to inform you that I am the Guest Editor of a Special Issue on "Stochastic Modelling and Applied Probability in Climatology and Medicine" for the journal Mathematics, and I am pleased to invite you to submit an article to be published in it.

This Special Issue aims to attract high-quality theoretical and experimental research articles in the range of fields mentioned. Special attention is devoted to the different aspects of climatology in the current context of global warming, such as the occurrence of extreme events and other relevant issues. In addition, the mathematical modelling of medical situations and related topics, such as the study of infectious diseases, e.g., COVID-19, is also another of the objectives of this Special Issue.

Hoping that this possibility is of interest to you.

Prof. Dr. Gerardo Sanz
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

19 pages, 1369 KiB  
Article
Bayesian Variable Selection in Generalized Extreme Value Regression: Modeling Annual Maximum Temperature
by Jorge Castillo-Mateo, Jesús Asín, Ana C. Cebrián, Jesús Mateo-Lázaro and Jesús Abaurrea
Mathematics 2023, 11(3), 759; https://doi.org/10.3390/math11030759 - 02 Feb 2023
Cited by 4 | Viewed by 1653
Abstract
In many applications, interest focuses on assessing relationships between covariates and the extremes of the distribution of a continuous response. For example, in climate studies, a usual approach to assess climate change has been based on the analysis of annual maximum data. Using [...] Read more.
In many applications, interest focuses on assessing relationships between covariates and the extremes of the distribution of a continuous response. For example, in climate studies, a usual approach to assess climate change has been based on the analysis of annual maximum data. Using the generalized extreme value (GEV) distribution, we can model trends in the annual maximum temperature using the high number of available atmospheric covariates. However, there is typically uncertainty in which of the many candidate covariates should be included. Bayesian methods for variable selection are very useful to identify important covariates. However, such methods are currently very limited for moderately high dimensional variable selection in GEV regression. We propose a Bayesian method for variable selection based on a stochastic search variable selection (SSVS) algorithm proposed for posterior computation. The method is applied to the selection of atmospheric covariates in annual maximum temperature series in three Spanish stations. Full article
(This article belongs to the Special Issue Stochastic Modelling and Applied Probability in Climatology and Medicine)
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26 pages, 461 KiB  
Article
A Stepwise Algorithm for Linearly Combining Biomarkers under Youden Index Maximization
by Rocío Aznar-Gimeno, Luis M. Esteban, Rafael del-Hoyo-Alonso, Ángel Borque-Fernando and Gerardo Sanz
Mathematics 2022, 10(8), 1221; https://doi.org/10.3390/math10081221 - 08 Apr 2022
Cited by 4 | Viewed by 1669
Abstract
Combining multiple biomarkers to provide predictive models with a greater discriminatory ability is a discipline that has received attention in recent years. Choosing the probability threshold that corresponds to the highest combined marker accuracy is key in disease diagnosis. The Youden index is [...] Read more.
Combining multiple biomarkers to provide predictive models with a greater discriminatory ability is a discipline that has received attention in recent years. Choosing the probability threshold that corresponds to the highest combined marker accuracy is key in disease diagnosis. The Youden index is a statistical metric that provides an appropriate synthetic index for diagnostic accuracy and a good criterion for choosing a cut-off point to dichotomize a biomarker. In this study, we present a new stepwise algorithm for linearly combining continuous biomarkers to maximize the Youden index. To investigate the performance of our algorithm, we analyzed a wide range of simulated scenarios and compared its performance with that of five other linear combination methods in the literature (a stepwise approach introduced by Yin and Tian, the min-max approach, logistic regression, a parametric approach under multivariate normality and a non-parametric kernel smoothing approach). The obtained results show that our proposed stepwise approach showed similar results to other algorithms in normal simulated scenarios and outperforms all other algorithms in non-normal simulated scenarios. In scenarios of biomarkers with the same means and a different covariance matrix for the diseased and non-diseased population, the min-max approach outperforms the rest. The methods were also applied on two real datasets (to discriminate Duchenne muscular dystrophy and prostate cancer), whose results also showed a higher predictive ability in our algorithm in the prostate cancer database. Full article
(This article belongs to the Special Issue Stochastic Modelling and Applied Probability in Climatology and Medicine)
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