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Applied Sciences
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Applied Biostatistics: Challenges and Opportunities
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Applied Biostatistics: Challenges and Opportunities

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 4073

Special Issue Editor

Dr. Ana Cristina Braga

E-Mail Website1 Website2
Guest Editor
Department of Production and Systems, ALGORITMI Center, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
Interests: applied statistics; biostatistics; computational statistics; ROC analysis; multivariate statistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the high complexity and dimension of the real world, the processing of data requires tools and methods capable of helping decision making.

Nowadays, health information is easier to capture and analyse than ever before given the developed data analytics. In the health sciences, biostatistics plays a central role in evaluating this data to better understand and tackle the health challenges that individuals and populations face across the globe.
This Special Issue aims to promote research works in computational statistics, scientific computation and applications in all areas of science involving a great volume of data or special datasets.

This Special Issue will focus on computational statistics, namely on new issues in the design of computational algorithms for implementing statistical methods, development in R, etc.; and applied biostatistics such as statistical case studies in all areas of science, including, medicine, biology, earth sciences and social sciences.

Topics of interest include (but are not limited to):

  • Statistical inference;
  • Statistical computing;
  • Biostatistics;
  • Reliability;
  • Survival analysis;
  • Design of experiments;
  • Multivariate analysis;
  • Nonparametric inference;
  • Statistical genetics;
  • Statistical quality control;
  • Survey sampling;
  • Computational Bayesian methods

This new Special Issue continues the previous Special Issue, “Applied Biostatistics & Statistical Computing”, which closed on 31 March 2024, including almost 10 valuable peer-reviewed papers. This issue continues to welcome scholars to contribute their new research.

Dr. Ana Cristina Braga
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. Applied Sciences 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 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
  • statistical computing
  • biostatistics
  • reliability
  • survival analysis
  • design of experiments
  • multivariate analysis
  • nonparametric inference
  • statistical genetics
  • statistical quality control
  • survey sampling
  • computational Bayesian methods

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Related Special Issue

Published Papers (3 papers)

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Research

17 pages, 3707 KiB  
Article
Extreme Value Index Estimation for Pareto-Type Tails under Random Censorship and via Generalized Means
by M. Ivette Gomes, Lígia Henriques-Rodrigues, M. Manuela Neves and Helena Penalva
Appl. Sci. 2024, 14(19), 8671; https://doi.org/10.3390/app14198671 - 26 Sep 2024
Cited by 1 | Viewed by 1003
Abstract
The field of statistical extreme value theory (EVT) focuses on estimating parameters associated with extreme events, such as the probability of exceeding a high threshold or determining a high quantile that lies at or beyond the observed data range. Typically, the assumption for [...] Read more.
The field of statistical extreme value theory (EVT) focuses on estimating parameters associated with extreme events, such as the probability of exceeding a high threshold or determining a high quantile that lies at or beyond the observed data range. Typically, the assumption for univariate data analysis is that the sample is complete, independent, identically distributed, or weakly dependent and stationary, drawn from an unknown distribution F. However, in the context of lifetime data, censoring is a common issue. In this work, we consider the case of random censoring for data with a heavy-tailed, Pareto-type distribution. As is common in applications of EVT, the estimation of the extreme value index (EVI) is critical, as it quantifies the tail heaviness of the distribution. The EVI has been extensively studied in the literature. Here, we discuss several classical EVI-estimators and reduced-bias (RB) EVI-estimators within a semi-parametric framework, with a focus on RB EVI-estimators derived from generalized means, which will be applied to both simulated and real survival data. Full article
(This article belongs to the Special Issue Applied Biostatistics: Challenges and Opportunities)
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21 pages, 2045 KiB  
Article
Forecasting Cost Risks of Corn and Soybean Crops through Monte Carlo Simulation
by Fernando Rodrigues de Amorim, Camila Carla Guimarães, Paulo Afonso and Maisa Sales Gama Tobias
Appl. Sci. 2024, 14(17), 8030; https://doi.org/10.3390/app14178030 - 8 Sep 2024
Viewed by 1807
Abstract
Considering that investing in the production of corn and soybeans is conditioned by production costs and several risks, the objective of this research work was to develop a simulation model for the prediction of the production costs of these commodities, considering the variability [...] Read more.
Considering that investing in the production of corn and soybeans is conditioned by production costs and several risks, the objective of this research work was to develop a simulation model for the prediction of the production costs of these commodities, considering the variability and correlation of key variables. The descriptive analysis of the data focused on measures such as mean, standard deviation, and coefficient of variation. To evaluate the relationship between commodity and input prices, Spearman’s demonstration coefficient and the coefficient of determination (R2) were used. A Monte Carlo simulation (MCS) was used to evaluate the variation in production costs and net revenues. The Predictor tool was used to make predictions based on historical data and time series models. This study was made for the period between 2018 and 2022 based on data provided by fifty companies from the state of São Paulo, Brazil. The results showed that the production cost/ha of corn faces a high-cost risk, particularly when production and market conditions are characterized by high levels of volatility, uncertainty, complexity, and ambiguity. The model proposed forecasts prices more accurately, as it considers the variation in the costs of inputs that most significantly influence the costs of corn and soybean crops. Full article
(This article belongs to the Special Issue Applied Biostatistics: Challenges and Opportunities)
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11 pages, 813 KiB  
Article
Optimal Concordant Tests
by Zhongxue Chen
Appl. Sci. 2024, 14(11), 4536; https://doi.org/10.3390/app14114536 - 25 May 2024
Viewed by 681
Abstract
In meta-analyses, unlike model-based methods, such as fixed- or random-effect models, the p-value combining methods are distribution-free and robust. How to appropriately and powerfully combine p-values obtained from various sources remains an important but challenging topic in statistical inference. For cases [...] Read more.
In meta-analyses, unlike model-based methods, such as fixed- or random-effect models, the p-value combining methods are distribution-free and robust. How to appropriately and powerfully combine p-values obtained from various sources remains an important but challenging topic in statistical inference. For cases where all or a majority of the individual alternative hypotheses have the same but unknown direction, concordant tests based on one-sided p-values can substantially improve the detecting power. However, there exists no test that is uniformly most powerful; therefore, figuring out how to choose a robust and powerful test to combine one-sided p-values for a given data set is desirable. In this paper, we propose and study a class of gamma distribution-based concordant tests. Those concordant tests are optimal under specific conditions. An asymptotically optimal concordant test is also studied. The excellent performances of the proposed tests were demonstrated through a numeric simulation study and real data example. Full article
(This article belongs to the Special Issue Applied Biostatistics: Challenges and Opportunities)
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