Brazilian Journal of Biometrics <p class="western" align="justify"><strong><span style="font-family: Arial;"><span style="font-family: Arial,serif;"><span lang="en-US">Promoting the development and application of statistical and data science methods to biological sciences. </span></span></span></strong><span style="font-family: Arial;"><span style="font-family: Arial,serif;"><span lang="en-US">The general objective of the journal is to publish original research papers that explore, promote and extend <span class="fontstyle0">statistical, mathematical and data science </span>methods in applied biological sciences.</span></span></span><span style="font-family: Arial;"><span style="font-family: Arial,serif;"><span lang="en-US"><br /></span></span></span></p> <p class="western" align="justify"><span style="font-family: Arial;"><span style="font-family: Arial,serif;"><span lang="en-US">Brazilian Journal of Biometrics is the official journal of the <a href="" target="_blank" rel="noopener">Brazilian Region of the International Biometric Society (RBras)</a>.</span></span></span></p> <p> </p> <table class="pkpTable" style="height: 443px;" role="grid" width="601" aria-labelledby="publicationDetailTableLabel"> <thead> <tr> <th class="" scope="col"> The articles Most Views</th> <th class="" scope="col">Abstract Views</th> <th class="" scope="col">File Views</th> <th class="" scope="col"> </th> <th class="" scope="col"> </th> <th class="" scope="col"> </th> <th class="-isActive" scope="col" aria-sort="true"> </th> </tr> </thead> <tbody> <tr class="pkpTable__row"> <td class="pkpTable__cell" tabindex="0"><a class="pkpStats__itemLink" href="" target="_blank" rel="noopener"><span class="pkpStats__itemAuthors">PEREIRA et al.</span> <span class="pkpStats__itemTitle">NAIVE STATISTICAL ANALYSES FOR COVID-19: APPLICATION TO DATA FROM BRAZIL AND ITALY</span></a></td> <td class="pkpTable__cell" tabindex="-1">10319</td> <td class="pkpTable__cell" tabindex="-1">68</td> <td class="pkpTable__cell" tabindex="-1"> </td> <td class="pkpTable__cell" tabindex="-1"> </td> <td class="pkpTable__cell" tabindex="-1"> </td> <td class="pkpTable__cell" tabindex="-1"> </td> </tr> <tr class="pkpTable__row"> <td class="pkpTable__cell" tabindex="-1"><a class="pkpStats__itemLink" href="" target="_blank" rel="noopener"><span class="pkpStats__itemAuthors">FERREIRA</span> <span class="pkpStats__itemTitle">SISVAR: A COMPUTER ANALYSIS SYSTEM TO FIXED EFFECTS SPLIT PLOT TYPE DESIGNS: Sisvar</span></a></td> <td class="pkpTable__cell" tabindex="-1">1151</td> <td class="pkpTable__cell" tabindex="-1">319</td> <td class="pkpTable__cell" tabindex="-1"> </td> <td class="pkpTable__cell" tabindex="-1"> </td> <td class="pkpTable__cell" tabindex="-1"> </td> <td class="pkpTable__cell" tabindex="-1"> </td> </tr> <tr class="pkpTable__row"> <td class="pkpTable__cell" tabindex="-1"><a class="pkpStats__itemLink" href="" target="_blank" rel="noopener"><span class="pkpStats__itemAuthors">FREITAS et al.</span> <span class="pkpStats__itemTitle">A CASE STUDY ON ANIMAL BEHAVIOR ANALYSIS USING GAMLSS</span></a></td> <td class="pkpTable__cell" tabindex="-1">179</td> <td class="pkpTable__cell" tabindex="-1">167</td> <td class="pkpTable__cell" tabindex="-1"> </td> <td class="pkpTable__cell" tabindex="-1"> </td> <td class="pkpTable__cell" tabindex="-1"> </td> <td class="pkpTable__cell" tabindex="-1"> </td> </tr> <tr class="pkpTable__row"> <td class="pkpTable__cell" tabindex="-1"><a class="pkpStats__itemLink" href="" target="_blank" rel="noopener"><span class="pkpStats__itemAuthors">LACHINI et al.</span> <span class="pkpStats__itemTitle">PESQUISA OPERACIONAL NA MINIMIZAÇÃO DE CUSTOS DE TRANSPORTE FLORESTAL</span></a></td> <td class="pkpTable__cell" tabindex="-1">142</td> <td class="pkpTable__cell" tabindex="-1">106</td> <td class="pkpTable__cell" tabindex="-1"> </td> <td class="pkpTable__cell" tabindex="-1"> </td> <td class="pkpTable__cell" tabindex="-1"> </td> <td class="pkpTable__cell" tabindex="-1"> </td> </tr> <tr class="pkpTable__row"> <td class="pkpTable__cell" tabindex="-1"><a class="pkpStats__itemLink" href="" target="_blank" rel="noopener"><span class="pkpStats__itemAuthors">LIMA et al.</span> <span class="pkpStats__itemTitle">SURVIVAL ANALYSIS APPLIED TO MEMBER TIME OF RETENTION TO THE HEALTH INSURANCE PROVIDER</span></a></td> <td class="pkpTable__cell" tabindex="-1">126</td> <td class="pkpTable__cell" tabindex="-1">100</td> <td class="pkpTable__cell" tabindex="-1"> </td> <td class="pkpTable__cell" tabindex="-1"> </td> <td class="pkpTable__cell" tabindex="-1"> </td> <td class="pkpTable__cell" tabindex="-1"> </td> </tr> </tbody> </table> <div class="journal-description"> </div> Editora UFLA - Universidade Federal de Lavras - UFLA en-US Brazilian Journal of Biometrics 2764-5290 <p><strong>Authors who publish with this journal agree to the following terms:</strong><br><br></p> <ol type="a"> <ol type="a"> <li><strong>Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a <a href="" target="_new">Creative Commons Attribution License</a> that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.</strong></li> <li><strong>Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.</strong></li> <li><strong>Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See <a href="" target="_new">The Effect of Open Access</a>).</strong></li> </ol> </ol> ALTERNATIVES TO THE CLASSICAL FREQUENTIST CONFIDENCE INTERVAL FOR DESCRIBING ZERO-INFLATED LEAF DISEASE SEVERITY <p>This paper presents the bootstrap percentile interval and the Bayesian credible interval as alternatives to the classical frequentist confidence interval for analysis of zero-inflated data. The indicated methods were applied to soybean downy mildew severity data obtained by stratified sampling in two municipalities in the state of São Paulo: Estiva Gerbi and Piracicaba. The amplitudes of the frequentist and bootstrap percentile confidence intervals were similar. For the Bayesian approach, the credible intervals of the posterior predictive distribution were considered using the zero-inflated beta distribution as likelihood. The credible intervals showed a wider range and included values in the upper bounds of the intervals greater than those observed in the data. We conclude that Bayesian inference is more complex, but allows incorporation of prior information regarding regional and seasonal aspects, contributing to better disease management in the field. When this information is not known, nonparametric bootstrap resampling is a simple alternative to construct intervals for zero-inflated data without assuming the distribution function.</p> Jhessica Letícia Kirch Brena Geliane Ferneda Fernando Henrique Silva Garcia Sonia Maria de Stefano Piedade Idemauro Antonio Rodrigues de Lara Copyright (c) 2022 Jhessica Letícia Kirch, Brena Geliane Ferneda, Fernando Henrique Silva Garcia, Sonia Maria de Stefano Piedade, Idemauro Antonio Rodrigues de Lara 2022-06-10 2022-06-10 40 2 10.28951/bjb.v40i2.563 FITTING EXTREME VALUE COPULAS WITH UNIMODAL CONVEX POLYNOMIAL REGRESSION USING BERNSTEIN POLYNOMIALS <p>Bernstein polynomials are suitable for performing shape-constrained regressions, in particular, for unimodal convex regression. The Pickands function is convex and unimodal, being a fundamental element in the theory of extreme value copulas. The purpose of this article is to explain in details the use of Bernstein polynomials in the estimation of Pickands function and to establish a new test of significance for extreme value copulas.</p> Danielle Gonçalves de Oliveira PRADO Lucas Monteiro CHAVES Devanil Jaques de SOUZA Eleanderson Campos EUGÊNIO FILHO Copyright (c) 2022 Danielle Gonçalves de Oliveira PRADO, Lucas Monteiro CHAVES, Devanil Jaques de SOUZA, Eleanderson Campos EUGÊNIO FILHO 2022-06-10 2022-06-10 40 2 10.28951/bjb.v40i2.548 ESTIMATION OF THE CRITICAL POINTS OF AN EPIDEMIC BY MEANS OF A LOGISTIC GROWTH MODEL <p>The study of epidemiological models are important because they help researchers to understand and propose possible strategies to combat any epidemic virus. Most of the research in those models, however, focuses on the response variable, modeling how it varies as a function of epidemiological parameters. In this paper, on the other hand, we focus on the explanatory variable ”time,” examining the critical points of the logistic model curve. These are: the maximum acceleration point(map), inflection point(ip), maximum deceleration point(mdp), and asymptotic deceleration point(adp). We first estimated a time series of the cases of people infected by COVID 19 as a function of time, and then used the cumulative estimates of the time series to fit a reparameterization of the logistic model. Data from China and Italy were used as an example, reporting the economic and political factors within each interval between the estimated critical points. The estimates of each critical point for China and Italy were respectively (map:34.93-50.92, ip:41.68-65.53;mdp:48.43-80.14;adp:57-94). This<br />methodology adds to the literature and shows researchers how the social, political, economic, and sanitary factors that were adopted in each of the countries influenced the difference of the intervals between the critical points in each country.</p> Ivan Bezerra ALLAMAN Enio Galinkin JELIHOVSCHI Copyright (c) 2022 Ivan Bezerra ALLAMAN, Enio Galinkin JELIHOVSCHI 2022-06-10 2022-06-10 40 2 10.28951/bjb.v40i2.576 CASE-FATALITY RATE BY COVID-19: A HIERARCHICAL BAYESIAN ANALYSIS OF COUNTRIES IN DIFFERENT REGIONS OF THE WORLD <p>The main goal of this study is the statistical analysis of deaths/cases epidemiologically defined as Case-Fatality Rate (CFR) due to novel coronavirus (SARSCoV-2) for 113 countries in different regions of the world in presence of some economic, health and social factors. The considered dataset refers to the accumulated daily counts of reported cases and deaths for a period ranging from the beginning of the COVID-19 pandemics in each country until July 25, 2020 the final follow-up day period. A binomial logistic regression model in presence of a random effect is assumed in the data analysis. The statistical analysis is considered under a hierarchical Bayesian approach using MCMC (Markov Chain Monte Carlo) methods do get the posterior summaries of interest.<br />The results we found are interesting considering the epidemiological interpretation, which could be of great interest to epidemiologists, health authorities, and the general public in the face of a complex pandemic in all its aspects, like the one we are experiencing.</p> Marcos Vininicius de Oliveira PERES Ricardo Puziol de OLIVEIRA Jorge Alberto ACHCAR Altacílio Aparecido NUNES Copyright (c) 2022 Marcos Vininicius de Oliveira PERES, Ricardo Puziol de OLIVEIRA, Jorge Alberto ACHCAR, Altacílio Aparecido NUNES 2022-06-10 2022-06-10 40 2 10.28951/bjb.v40i2.565 SELECTION OF COVARIATES IN A LOGISTIC REGRESSION MODEL FOR THE PREDICTION OF RESISTANCE TO RICE BLAST <p>Rice (<em>Oryza sativa</em> L.) has been one of the most consumed foods on the planet, with economic and social importance. Diseases, mainly blast, caused by the fungus <em>Pyricularia oryzae</em>, are limiting factors for the production of rice. The present work aimed to select covariables that can influence resistance to rice blast, using the selection strategy proposed by Collett. Logistic regression models were adjusted to predict disease resistance, using the ROC curve to assess the predictive capacity. The data used were obtained from a population of 413 plants, with phenotypic information collected in 82 countries and classified into five subpopulations. The research found that, out of over fifteen variables embedded to assess the disease, only three revealed to be relevant for the final adjusted model, namely: width of flag leaf (V4), the mean number of primary panicle branches (V8) and the amount of amylose from ground grains (V15). The variable V4 presented the most significant influence on disease resistance. Additionally, for each unit increase in V4, V8 and V15, it is expected to obtain 279.3, 31.9 and 9.4% increases, respectively, in the probability of resistance to rice blast.</p> Momate Emate OSSIFO Marciel Lelis Duarte Antônio Policarpo Souza CARNEIRO Vinicios Silva dos Santos Sebastião Martins Filho Copyright (c) 2022 Momate Emate OSSIFO, Marciel Lelis Duarte, Antônio Policarpo Souza CARNEIRO, Vinicios Silva dos Santos, Sebastião Martins Filho 2022-06-10 2022-06-10 40 2 10.28951/bjb.v40i2.559 DESCRIPTION OF HEIGHT GROWTH OF HYBRID EUCALYPTUS CLONES IN SEMI-ARID REGION USING NON-LINEAR MODELS <p>Brazil stands out worldwide for planting homogeneous forests, mainly pine and eucalyptus. Forestry production is of great importance for the country’s economy, being also a reference in sustainability, competitiveness and innovation. Of the 10 million hectares of planted trees, 76.3% is composed of the genus<em> Eucalyptus</em>, which makes Brazil one of the largest producers of this genus in the world. The analysis of the growth trajectory of trees of this genus can be a great ally in improving the management plans currently used. In this sense, the aim of this study was to compare the performance of the nonlinear models Gompertz, von Bertalanffy, Brody, Chapman-Richards and Schöngart, which were fit using the R software considering the first order autoregressive error structure (AR1), applied to data of average height, in meters, in relation to time, in months, totaling 15 observations obtained during six and a half years. Nonlinearity measures were used to check the adequacy of the linear approximations of models and as criteria for model selection the R<sup>2</sup>, AIC<sub>C</sub> and DPR, with the Schöngart (AR1) model being the one that best fit the data.</p> Ariana Campos FRUHAUF Édipo Menezes da SILVA Daniela GRANATO-SOUZA Edilson Marcelino SILVA Joel Augusto MUNIZ Tales Jesus FERNANDES Copyright (c) 2022 Ariana Campos FRUHAUF, Édipo Menezes da SILVA, Daniela GRANATO-SOUZA, Edilson Marcelino SILVA, Joel Augusto MUNIZ, Tales Jesus FERNANDES 2022-06-10 2022-06-10 40 2 10.28951/bjb.v40i2.543 ROC APP: AN APPLICATION TO UNDERSTAND ROC CURVES <p>We present a software application ( to help students understand the Receiver Operating Characteristic (ROC) curve and other concepts associated with binary classification models. We use the diagnostic test scenario as a motivation to explain the underlying concepts and the app functionalities. The ROC App enables students to interactively learn why/how the ROC curve closely relates to the accuracy rates, by seeing how these curves and rates respond to modifications on the population’s parameters.</p> George Freitas VON BORRIES Allan Vieira de Castro QUADROS Copyright (c) 2022 Georges Freitas von BORRIES, Allan Vieira de Castro QUADROS 2022-06-10 2022-06-10 40 2 10.28951/bjb.v40i2.566