G. F. Oliveira, T. B. R. Miranda, A. C. Nascimento, M. Nascimento, E. T. Caixeta, Leliane Raynne Duarte Silva, Emilly Ruas Alkimim, F. D. Silva
ABSTRACT: Coffee growing is one of the most important agricultural activities in the world market. Coffea canephora is one of the most commercially relevant species, which can be divided into the varietal groups Conilon and Robusta. These varietal groups have complementary agronomic interests. Therefore, hybrids are obtained through crosses between these groups. Given the difficulty in differentiating the two varietal groups of genotypes in the field, the correct discrimination is essential for the definition of crosses in breeding programs. In this context, the present work aimed to apply a discriminant analysis (DA) to define functions to differentiate between varietal groups and hybrids of C. canephora, and identify the most relevant phenotypic traits in these functions. Data from 165 genotypes from the Instituto Capixaba de Pesquisa, Assistência Técnica e Extensão Rural e do Centro Agronómico Tropical de Investigación y Enseñanza were used to measure different plant traits. It was applied the quadratic DA with the best performance for genotype discrimination, with an average apparent error rate of 0.0364. Cercosporiose incidence, rust incidence, vegetative vigor, plant height and diameter of the canopy projection were the most important traits in the varietal groups' discrimination.
摘要:咖啡种植是世界市场上最重要的农业活动之一。canephora咖啡是最具商业价值的品种之一，它可以分为Conilon和Robusta两个品种群。这些品种群具有互补的农艺利益。因此，杂种是通过这些群体之间的杂交获得的。考虑到在田间区分两个基因型品种群的困难，正确的区分对于育种计划中杂交的定义至关重要。在此背景下，本研究旨在应用判别分析(DA)来定义canephora品种群和杂交种的功能，并确定这些功能中最相关的表型性状。来自Capixaba de Pesquisa Instituto, Assistência t cnica e extenss o Rural e do Centro Agronómico Tropical de Investigación y Enseñanza的165个基因型的数据被用于测量不同的植物性状。采用二次DA进行基因型判别效果最佳，平均表观误差率为0.0364。菌丝病发病率、锈病发病率、营养活力、株高和冠突直径是品种群鉴别的重要性状。
{"title":"DISCRIMINATION OF VARIETAL GROUPS AND HYBRIDS OF COFFEA CANEPHORA SPECIES USING MULTIVARIATE ANALYSIS","authors":"G. F. Oliveira, T. B. R. Miranda, A. C. Nascimento, M. Nascimento, E. T. Caixeta, Leliane Raynne Duarte Silva, Emilly Ruas Alkimim, F. D. Silva","doi":"10.28951/RBB.V39I1.525","DOIUrl":"https://doi.org/10.28951/RBB.V39I1.525","url":null,"abstract":" ABSTRACT: Coffee growing is one of the most important agricultural activities in the world market. Coffea canephora is one of the most commercially relevant species, which can be divided into the varietal groups Conilon and Robusta. These varietal groups have complementary agronomic interests. Therefore, hybrids are obtained through crosses between these groups. Given the difficulty in differentiating the two varietal groups of genotypes in the field, the correct discrimination is essential for the definition of crosses in breeding programs. In this context, the present work aimed to apply a discriminant analysis (DA) to define functions to differentiate between varietal groups and hybrids of C. canephora, and identify the most relevant phenotypic traits in these functions. Data from 165 genotypes from the Instituto Capixaba de Pesquisa, Assistência Técnica e Extensão Rural e do Centro Agronómico Tropical de Investigación y Enseñanza were used to measure different plant traits. It was applied the quadratic DA with the best performance for genotype discrimination, with an average apparent error rate of 0.0364. Cercosporiose incidence, rust incidence, vegetative vigor, plant height and diameter of the canopy projection were the most important traits in the varietal groups' discrimination.","PeriodicalId":36293,"journal":{"name":"Revista Brasileira de Biometria","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77983738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Edmary Silveira Barreto Araújo, J. D. Scalon, L. S. Batista
A spatial point pattern is a collection of points irregularly located within a bounded area (2D) or space (3D) that have been generated by some form of stochastic mechanism. Examples of point patterns include locations of trees in a forest, of cases of a disease in a region, or of particles in a microscopic section of a composite material. Spatial Point pattern analysis is used mostly to determine the absence (completely spatial randomness) or presence (regularity and clustering) of spatial dependence structure of the locations. Methods based on the space domain are widely used for this purpose, while methods conducted in the frequency domain (spectral analysis) are still unknown to most researchers. Spectral analysis is a powerful tool to investigate spatial point patterns, since it does not assume any structural characteristics of the data (ex. isotropy), and uses only the autocovariance function, and its Fourier transform. There are some methods based on the spectral frameworks for analyzing 2D spatial point patterns. There is no such methods available for the 3D situation and, therefore, the aim of this work is to develop new methods based on spectral framework for the analysis of three-dimensional point patterns. The emphasis is on relating periodogram structure to the type of stochastic process which could have generated a 3D observed pattern. The results show that the methods based on spectral analysis developed in this work are able to identify patterns of three typical three-dimensional point processes, and can be used, concurrently, with analyzes in the space domain for a better characterization of spatial point patterns.
{"title":"EXPLORATORY SPECTRAL ANALYSIS IN THREE-DIMENSIONAL SPATIAL POINT PATTERNS","authors":"Edmary Silveira Barreto Araújo, J. D. Scalon, L. S. Batista","doi":"10.28951/RBB.V39I1.524","DOIUrl":"https://doi.org/10.28951/RBB.V39I1.524","url":null,"abstract":"A spatial point pattern is a collection of points irregularly located within a bounded area (2D) or space (3D) that have been generated by some form of stochastic mechanism. Examples of point patterns include locations of trees in a forest, of cases of a disease in a region, or of particles in a microscopic section of a composite material. Spatial Point pattern analysis is used mostly to determine the absence (completely spatial randomness) or presence (regularity and clustering) of spatial dependence structure of the locations. Methods based on the space domain are widely used for this purpose, while methods conducted in the frequency domain (spectral analysis) are still unknown to most researchers. Spectral analysis is a powerful tool to investigate spatial point patterns, since it does not assume any structural characteristics of the data (ex. isotropy), and uses only the autocovariance function, and its Fourier transform. There are some methods based on the spectral frameworks for analyzing 2D spatial point patterns. There is no such methods available for the 3D situation and, therefore, the aim of this work is to develop new methods based on spectral framework for the analysis of three-dimensional point patterns. The emphasis is on relating periodogram structure to the type of stochastic process which could have generated a 3D observed pattern. The results show that the methods based on spectral analysis developed in this work are able to identify patterns of three typical three-dimensional point processes, and can be used, concurrently, with analyzes in the space domain for a better characterization of spatial point patterns.","PeriodicalId":36293,"journal":{"name":"Revista Brasileira de Biometria","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82688294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anderson Cristiano Neisse, F. L. Oliveira, A. Oliveira, F. Cruz, Raimundo Marques do Nascimento Neto
▪ ABSTRACT: Characterized by persistent fatigue, pain, cognitive impairment and sleep difficulties, Chronic Fatigue Syndrome (CFS) has been common in clinical practice. Studies indicate multiple factors contributing to CFS development: poor sleep, dehydration, psychological stress, hormonal dysfunction, nutrient deficiencies, among others. In risk work conditions, like the shift work of mines, CFS significantly increases the chance of fatal accidents. Work environments of mines suggest the presence of factors that increase the risk of developing CFS. Considering the severity/implications of CFS’s symptoms on the social and professional lives as well as on the economy, efforts are targeting its characterization and prevention. This study aims to assess the risk of CFS by studying cross-sectional data on absenteeism of 621 shift workers, measuring 8 anthropometric and 11 biochemical variables as well as age and gender, amounting 21 variables. After imputation, logistic regression was fitted by Stepwise selection, Lasso and Elastic-Net regularization. Results suggest that the models do not discriminate very well due to noise inherent to the dependent variable. However, all models agree on the effects of Sodium and Total Cholesterol on the risk of absenteeism. The Stepwise model also indicates LDL and Triglycerides as significant factors, both Lasso and Elastic-Net show effects for LDL instead. The Elastic-Net model suggests an effect of Potassium, though inconclusive according to the literature.
{"title":"CHRONIC FATIGUE SYNDROME AND ITS RELATION WITH ABSENTEEISM: ELASTIC-NET AND STEPWISE APPLIED TO BIOCHEMICAL AND ANTHROPOMETRIC CLINICAL MEASUREMENTS","authors":"Anderson Cristiano Neisse, F. L. Oliveira, A. Oliveira, F. Cruz, Raimundo Marques do Nascimento Neto","doi":"10.28951/RBB.V39I1.533","DOIUrl":"https://doi.org/10.28951/RBB.V39I1.533","url":null,"abstract":"▪ ABSTRACT: Characterized by persistent fatigue, pain, cognitive impairment and sleep difficulties, Chronic Fatigue Syndrome (CFS) has been common in clinical practice. Studies indicate multiple factors contributing to CFS development: poor sleep, dehydration, psychological stress, hormonal dysfunction, nutrient deficiencies, among others. In risk work conditions, like the shift work of mines, CFS significantly increases the chance of fatal accidents. Work environments of mines suggest the presence of factors that increase the risk of developing CFS. Considering the severity/implications of CFS’s symptoms on the social and professional lives as well as on the economy, efforts are targeting its characterization and prevention. This study aims to assess the risk of CFS by studying cross-sectional data on absenteeism of 621 shift workers, measuring 8 anthropometric and 11 biochemical variables as well as age and gender, amounting 21 variables. After imputation, logistic regression was fitted by Stepwise selection, Lasso and Elastic-Net regularization. Results suggest that the models do not discriminate very well due to noise inherent to the dependent variable. However, all models agree on the effects of Sodium and Total Cholesterol on the risk of absenteeism. The Stepwise model also indicates LDL and Triglycerides as significant factors, both Lasso and Elastic-Net show effects for LDL instead. The Elastic-Net model suggests an effect of Potassium, though inconclusive according to the literature.","PeriodicalId":36293,"journal":{"name":"Revista Brasileira de Biometria","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82822800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A Rasch Poisson counts (RPC) model is described to identify individual latent traits and facilities of the items of tests that model the error (or success) count in several tasks over time, instead of modeling the correct responses to items in a test as in the dichotomous item response theory (IRT) model. These types of tests can be more informative than traditional tests. To estimate the model parameters, we consider a Bayesian approach using the integrated nested Laplace approximation (INLA). We develop residual analysis to assess model fit by introducing randomized quantile residuals for items. The data used to illustrate the method comes from 228 people who took a selective attention test. The test has 20 blocks (items), with a time limit of 15 seconds for each block. The results of the residual analysis of the RPC were promising and indicated that the studied attention data are not well fitted by the RPC model.
{"title":"RESIDUAL ANALYSIS IN RASCH POISSON COUNTS MODELS","authors":"Naiara Santos, Jorge L. Bazán","doi":"10.28951/RBB.V39I1.531","DOIUrl":"https://doi.org/10.28951/RBB.V39I1.531","url":null,"abstract":"A Rasch Poisson counts (RPC) model is described to identify individual latent traits and facilities of the items of tests that model the error (or success) count in several tasks over time, instead of modeling the correct responses to items in a test as in the dichotomous item response theory (IRT) model. These types of tests can be more informative than traditional tests. To estimate the model parameters, we consider a Bayesian approach using the integrated nested Laplace approximation (INLA). We develop residual analysis to assess model fit by introducing randomized quantile residuals for items. The data used to illustrate the method comes from 228 people who took a selective attention test. The test has 20 blocks (items), with a time limit of 15 seconds for each block. The results of the residual analysis of the RPC were promising and indicated that the studied attention data are not well fitted by the RPC model.","PeriodicalId":36293,"journal":{"name":"Revista Brasileira de Biometria","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76449148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Suellen Teixeira Zavadzki DE Pauli, M. Kleina, W. H. Bonat
The machine learning area has recently gained prominence and artificial neural networks are among the most popular techniques in this field. Such techniques have the learning capacity that occurs during an iterative process of model fitting. Multilayer perceptron (MLP) is one of the first networks that emerged and, for this architecture, backpropagation and its modifications are widely used learning algorithms. In this article, the learning of the MLP neural network was approached from the Bayesian perspective by using Monte Carlo via Markov Chains (MCMC) simulations. The MLP architecture consists of the input, hidden and output layers. In the structure, there are several weights that connect each neuron in each layer. The input layer is composed of the covariates of the model. In the hidden layer there are activation functions. In the output layer, there are the result which is compared with the observed value and the loss function is calculated. We analyzed the network learning through simulated data of known weights in order to understand the estimation by the Bayesian method. Subsequently, we predicted the price of WTI oil and obtained a credibility interval for the forecasts. We provide an R implementation and the datasets as supplementary materials.
{"title":"MULTILAYER PERCEPTRON ARTIFICIAL NEURAL NETWORKS: AN APPROACH FOR LEARNING THROUGH THE BAYESIAN FRAMEWORK","authors":"Suellen Teixeira Zavadzki DE Pauli, M. Kleina, W. H. Bonat","doi":"10.28951/RBB.V39I1.495","DOIUrl":"https://doi.org/10.28951/RBB.V39I1.495","url":null,"abstract":"The machine learning area has recently gained prominence and artificial neural networks are among the most popular techniques in this field. Such techniques have the learning capacity that occurs during an iterative process of model fitting. Multilayer perceptron (MLP) is one of the first networks that emerged and, for this architecture, backpropagation and its modifications are widely used learning algorithms. In this article, the learning of the MLP neural network was approached from the Bayesian perspective by using Monte Carlo via Markov Chains (MCMC) simulations. The MLP architecture consists of the input, hidden and output layers. In the structure, there are several weights that connect each neuron in each layer. The input layer is composed of the covariates of the model. In the hidden layer there are activation functions. In the output layer, there are the result which is compared with the observed value and the loss function is calculated. We analyzed the network learning through simulated data of known weights in order to understand the estimation by the Bayesian method. Subsequently, we predicted the price of WTI oil and obtained a credibility interval for the forecasts. We provide an R implementation and the datasets as supplementary materials.","PeriodicalId":36293,"journal":{"name":"Revista Brasileira de Biometria","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83828882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ABSTRACT: This article addressed traffic accidents in the urban perimeter of the city of LondrinaParaná, Brazil, carried out in 2019. The information used was from the Integrated Emergency Trauma Care System and collected through the General Occurrence Registry (RGO) of the Fire Department of Londrina. For this study, accidents were selected in the collision category between vehicle drivers and motorized drivers. The study variables were the most frequent months and the accident codes defined by Integrated Trauma Emergency Service (SIATE). The article aimed to analyze the occurrences (collision) of vehicle drivers in 2019, seeking to make the population aware of accident prevention. The methodology adopted was descriptive, quantitative, obtained intentionally. The results show that these categories of drivers are responsible for more than 50% of the total accidents and more than 60% of the fatal victims. According to the results obtained, it is expected that the authorities may be sensitized, adopting measures that lead those responsible to more rigorous sanctions, thus making drivers aware of having greater responsibility when driving a vehicle, emphasizing that they need to redouble their attention and have responsibility in traffic, avoiding negligence and damage to the lives of others.
{"title":"A STATISTICAL STUDY OF TRAFFIC ACCIDENTS IN THE CITY OF LONDRINA-PR IN 2019","authors":"Meiri das Graças Cardoso, R. Martins, L. Sturion","doi":"10.28951/RBB.V39I1.503","DOIUrl":"https://doi.org/10.28951/RBB.V39I1.503","url":null,"abstract":" ABSTRACT: This article addressed traffic accidents in the urban perimeter of the city of LondrinaParaná, Brazil, carried out in 2019. The information used was from the Integrated Emergency Trauma Care System and collected through the General Occurrence Registry (RGO) of the Fire Department of Londrina. For this study, accidents were selected in the collision category between vehicle drivers and motorized drivers. The study variables were the most frequent months and the accident codes defined by Integrated Trauma Emergency Service (SIATE). The article aimed to analyze the occurrences (collision) of vehicle drivers in 2019, seeking to make the population aware of accident prevention. The methodology adopted was descriptive, quantitative, obtained intentionally. The results show that these categories of drivers are responsible for more than 50% of the total accidents and more than 60% of the fatal victims. According to the results obtained, it is expected that the authorities may be sensitized, adopting measures that lead those responsible to more rigorous sanctions, thus making drivers aware of having greater responsibility when driving a vehicle, emphasizing that they need to redouble their attention and have responsibility in traffic, avoiding negligence and damage to the lives of others.","PeriodicalId":36293,"journal":{"name":"Revista Brasileira de Biometria","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84020235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
W. D. S. E. Silva, F. A. Fernandes, F. R. Muniz, J. A. Muniz, T. J. Fernandes
Brazil is a major producer in the timber sector, mainly with the use of wood from species of the genus Eucalyptus, with 26.1% of planted forests located in Minas Gerais. Researchers and manufacturers have been searching for techniques with the objective of making full use of these forests, with a primary focus on greater growth. A modeling of growth curves is an alternative for the estimation of floral production and an important aid tool for the researcher’s decision making. Growth curves are commonly studied by nonlinear regression models, which have important assumptions that if not met should be added to the model. The present work aims to select among nonlinear Logistic, Gompertz and von Bertalanffy regression models the most suitable to describe the growth in wood volume of Eucalyptus urophylla x Eucalyptus grandis hybrids in three Forest Site categories, including whether assumption deviations are required. Methods were executed by the Gauss-Newton iterative method implemented in nls() and it gnls() functions of the R software. Determination coefficient, Akaike information criterion (AICc) and Residual Standard Deviation (RSD) were used as selection evaluators of the best model. The results demonstrate that for all site categories, the Gompertz model with addition of autoregressive parameters AR (1) is the most appropriate to describe the growth in wood volume of Eucalyptus urophylla x Eucalyptus grandis hybrids. The addition of the first-order autoregressive parameter does not affect the quality of fit, but it is the correct procedure. Site I, which presents the largest trees according to pre-defined variations, recorded 308 m/ha of wood volume, followed by 286 m/ha and 263 m/ha for Sites II and III, respectively. The time for Site III to reach the maximum point of volume growth is between the fourth and sixth year, while the other sites are more precocious, reaching this point between the second and third year. 1Universidade Federal de Lavras UFLA, Instituto de Ciências Exatas e Tecnológicas, Departamento de Estat́ıstica, CEP: 37200-900, Lavras, MG, Brasil. E-mail: walleffsilva@gmail.com; fernandesfelipest@gmail.com; joamuniz@ufla.br; tales.jfernandes@ufla.br 2Escola Superior de Agricultura “Luiz de Queiroz” Esalq-USP, CEP: 13418-900, Piracicaba, SP, Brasil. E-mail: fabiana.muniz@hotmail.com.br 122 Rev. Bras. Biom., Lavras, v.39, n.1, p.122-138, 2021 doi: 10.28951/rbb.v39i1.511
巴西是木材行业的主要生产国，主要使用桉树属的木材，其26.1%的人工林位于米纳斯吉拉斯州。研究人员和制造商一直在寻找充分利用这些森林的技术，其主要重点是促进生长。生长曲线模型是估算花卉产量的一种替代方法，是研究人员决策的重要辅助工具。生长曲线通常是通过非线性回归模型来研究的，这些模型有重要的假设，如果不满足这些假设，就应该添加到模型中。本研究旨在从非线性Logistic回归模型、Gompertz回归模型和von Bertalanffy回归模型中选择最适合描述尾桉与大桉杂交品种在3个立地类别中木材体积增长的模型，包括是否需要假设偏差。方法通过在R软件的nls()和它的gnls()函数中实现的高斯-牛顿迭代法执行。采用决定系数、赤池信息准则(AICc)和残差标准偏差(RSD)作为最佳模型的选择评价指标。结果表明，在所有样地类别中，加入自回归参数AR(1)的Gompertz模型最适合描述尾桉与大桉杂交树种的材积增长情况。一阶自回归参数的加入不影响拟合质量，但这是正确的过程。根据预先定义的变化，Site I呈现出最大的树木，记录了308 m/ha的木材体积，其次是Site II和Site III，分别为286 m/ha和263 m/ha。站点III达到体积增长最大值的时间是在第4年到第6年之间，而其他站点则更早熟，在第2年到第3年之间达到这个点。1拉夫拉斯联邦大学，Ciências Exatas研究所Tecnológicas, Estat系ıstica, CEP: 37200-900，巴西，MG，拉夫拉斯。电子邮件:walleffsilva@gmail.com;fernandesfelipest@gmail.com;joamuniz@ufla.br;tales.jfernandes@ufla.br 2Escola Superior de Agricultura " Luiz de Queiroz " Esalq-USP, CEP: 13418-900，皮拉西卡巴，SP，巴西。电子邮件:fabiana.muniz@hotmail.com.br 122牧师。Biom。[j] .中国科学院学报，vol .39, no .1, p.122-138, 2021 doi: 10.28951/rbb.v39i1.511
{"title":"EUCALYPTUS GRANDIS X EUCALYPTUS UROPHYLLA GROWTH CURVE IN DIFFERENT SITE CLASSIFICATIONS, CONSIDERING RESIDUAL AUTOCORRELATION","authors":"W. D. S. E. Silva, F. A. Fernandes, F. R. Muniz, J. A. Muniz, T. J. Fernandes","doi":"10.28951/RBB.V39I1.511","DOIUrl":"https://doi.org/10.28951/RBB.V39I1.511","url":null,"abstract":"Brazil is a major producer in the timber sector, mainly with the use of wood from species of the genus Eucalyptus, with 26.1% of planted forests located in Minas Gerais. Researchers and manufacturers have been searching for techniques with the objective of making full use of these forests, with a primary focus on greater growth. A modeling of growth curves is an alternative for the estimation of floral production and an important aid tool for the researcher’s decision making. Growth curves are commonly studied by nonlinear regression models, which have important assumptions that if not met should be added to the model. The present work aims to select among nonlinear Logistic, Gompertz and von Bertalanffy regression models the most suitable to describe the growth in wood volume of Eucalyptus urophylla x Eucalyptus grandis hybrids in three Forest Site categories, including whether assumption deviations are required. Methods were executed by the Gauss-Newton iterative method implemented in nls() and it gnls() functions of the R software. Determination coefficient, Akaike information criterion (AICc) and Residual Standard Deviation (RSD) were used as selection evaluators of the best model. The results demonstrate that for all site categories, the Gompertz model with addition of autoregressive parameters AR (1) is the most appropriate to describe the growth in wood volume of Eucalyptus urophylla x Eucalyptus grandis hybrids. The addition of the first-order autoregressive parameter does not affect the quality of fit, but it is the correct procedure. Site I, which presents the largest trees according to pre-defined variations, recorded 308 m/ha of wood volume, followed by 286 m/ha and 263 m/ha for Sites II and III, respectively. The time for Site III to reach the maximum point of volume growth is between the fourth and sixth year, while the other sites are more precocious, reaching this point between the second and third year. 1Universidade Federal de Lavras UFLA, Instituto de Ciências Exatas e Tecnológicas, Departamento de Estat́ıstica, CEP: 37200-900, Lavras, MG, Brasil. E-mail: walleffsilva@gmail.com; fernandesfelipest@gmail.com; joamuniz@ufla.br; tales.jfernandes@ufla.br 2Escola Superior de Agricultura “Luiz de Queiroz” Esalq-USP, CEP: 13418-900, Piracicaba, SP, Brasil. E-mail: fabiana.muniz@hotmail.com.br 122 Rev. Bras. Biom., Lavras, v.39, n.1, p.122-138, 2021 doi: 10.28951/rbb.v39i1.511","PeriodicalId":36293,"journal":{"name":"Revista Brasileira de Biometria","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85158102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maria Eugênia de Oliveira Batista, Rafaela da Silva Gomes, Luciene Resende Gonçalves
The Bovespa Index is the most important indicator of the average performance of quotations on the Brazilian stock market, portraying the behavior of the main securities traded on the Bovespa. The Petrobras Index refers to Petrobras quotations on the São Paulo Stock Exchange (Bovespa). In view of the expressiveness of these indices, the objective of this work will be to perform a univariate and multivariate temporal analysis of the series of returns of Petrobrás and Ibovespa in two periods. The first period is comprised between 2005 and 2015, while the second between 2015 and 2019. This division was intended to divide the pre and post crisis periods in the face of complaints of corruption against Petrobras, which caused a greater variation in liquidity in the company's shares. The survey is ongoing and only the analysis of the first period is complete. The series data were taken from the IPEADATA website (ipeadata.gov.br), then deflated, and then their returns were calculated using software R. This is done in two steps. In the first, the data are modeled using the ARIMA model, and in the second, the GARCH is fitted to the squared residues. For the Bovespa index series, an ARIMA (3.0.3) and then GARCH (1.1) were fitted. For the Petrobras series of returns, first, an incomplete ARMA was fittted with the autoregressive parameters of significant order 6 and the order 3 of moving averages also statistically significant. The volatility, in this case, was also fitted by a GARCH (1.1). The rates of the two returns were useful. A multivariate analysis indicated that, during the analyzed lag period, the Petrobras index did not directly influence the Bovespa index, although in the analyzed period it was chosen before the denunciation and corruption scandal. After analyzing the second period, a comparison will be made between the fits.
{"title":"UNIVARIATE AND MULTIVARIATE ANALYSIS OF THE BOVESPA AND PETROBRAS INDICES BETWEEN 2005-2015","authors":"Maria Eugênia de Oliveira Batista, Rafaela da Silva Gomes, Luciene Resende Gonçalves","doi":"10.28951/RBB.V39I1.513","DOIUrl":"https://doi.org/10.28951/RBB.V39I1.513","url":null,"abstract":"The Bovespa Index is the most important indicator of the average performance of quotations on the Brazilian stock market, portraying the behavior of the main securities traded on the Bovespa. The Petrobras Index refers to Petrobras quotations on the São Paulo Stock Exchange (Bovespa). In view of the expressiveness of these indices, the objective of this work will be to perform a univariate and multivariate temporal analysis of the series of returns of Petrobrás and Ibovespa in two periods. The first period is comprised between 2005 and 2015, while the second between 2015 and 2019. This division was intended to divide the pre and post crisis periods in the face of complaints of corruption against Petrobras, which caused a greater variation in liquidity in the company's shares. The survey is ongoing and only the analysis of the first period is complete. The series data were taken from the IPEADATA website (ipeadata.gov.br), then deflated, and then their returns were calculated using software R. This is done in two steps. In the first, the data are modeled using the ARIMA model, and in the second, the GARCH is fitted to the squared residues. For the Bovespa index series, an ARIMA (3.0.3) and then GARCH (1.1) were fitted. For the Petrobras series of returns, first, an incomplete ARMA was fittted with the autoregressive parameters of significant order 6 and the order 3 of moving averages also statistically significant. The volatility, in this case, was also fitted by a GARCH (1.1). The rates of the two returns were useful. A multivariate analysis indicated that, during the analyzed lag period, the Petrobras index did not directly influence the Bovespa index, although in the analyzed period it was chosen before the denunciation and corruption scandal. After analyzing the second period, a comparison will be made between the fits.","PeriodicalId":36293,"journal":{"name":"Revista Brasileira de Biometria","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78187189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article is a direct consequence of the authors’ desire to discuss the role of statistics in data analysis. The analysis of coronavirus (COVID-19) databases are used as to show simple, but powerful statistical frameworks. We do believe that models for assessing future trends in temporal data in general, and in cases and/or deaths of COVID-19, belongs to the area of (Bio)Statistics. Just as engineers use knowledge of physics, chemistry and often architecture, when constructing bridges, buildings and roads, statisticians use knowledge of mathematics, computer science and even physics for modelling, analysing, and forecasting in order to transform data into information. While the statistician’s contribution is rarely acknowledged, everyone knows that a building is a work of an engineer. Nonetheless, nowadays statistics has been gaining the attention that it deserves due to the rise of big data and data science that was built on the foundations of statistics. This article shows that, even with only basic knowledge of statistics, one can adequately collaborate with the community in dealing with very important issues such as the COVID-19 numbers. In order to model and to obtain predictions we use well-known distributions to statisticians working on survival analysis: gamma, Weibull and log-normal distributions. We also make use of singular spectrum analysis, a simple non-parametric time series methodology, for an analogous purpose. Survival analysis is a research area widely used in Biostatistics and even in Reliability, while time series analysis is widely used across areas where the data is measured along the time.
{"title":"NAIVE STATISTICAL ANALYSES FOR COVID-19: APPLICATION TO DATA FROM BRAZIL AND ITALY","authors":"C. Pereira, L. R. Nakamura, P. Rodrigues","doi":"10.28951/RBB.V39I1.515","DOIUrl":"https://doi.org/10.28951/RBB.V39I1.515","url":null,"abstract":"This article is a direct consequence of the authors’ desire to discuss the role of statistics in data analysis. The analysis of coronavirus (COVID-19) databases are used as to show simple, but powerful statistical frameworks. We do believe that models for assessing future trends in temporal data in general, and in cases and/or deaths of COVID-19, belongs to the area of (Bio)Statistics. Just as engineers use knowledge of physics, chemistry and often architecture, when constructing bridges, buildings and roads, statisticians use knowledge of mathematics, computer science and even physics for modelling, analysing, and forecasting in order to transform data into information. While the statistician’s contribution is rarely acknowledged, everyone knows that a building is a work of an engineer. Nonetheless, nowadays statistics has been gaining the attention that it deserves due to the rise of big data and data science that was built on the foundations of statistics. This article shows that, even with only basic knowledge of statistics, one can adequately collaborate with the community in dealing with very important issues such as the COVID-19 numbers. In order to model and to obtain predictions we use well-known distributions to statisticians working on survival analysis: gamma, Weibull and log-normal distributions. We also make use of singular spectrum analysis, a simple non-parametric time series methodology, for an analogous purpose. Survival analysis is a research area widely used in Biostatistics and even in Reliability, while time series analysis is widely used across areas where the data is measured along the time.","PeriodicalId":36293,"journal":{"name":"Revista Brasileira de Biometria","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74660128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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