Julio Cezar Souza Vasconcelos

This study proposes a flexible three-parameter distribution to capture complex patterns in continuous positive data. By combining the generalized log-logistic odd generator with an error function distribution, our model successfully accommodates various density shapes, including strong skewness and bimodality. Using Monte Carlo simulations and maximum likelihood estimation, we validate our regression model’s estimators and confirm that larger sample sizes yield high precision, consistency, and inferential stability. Using data from hospitalized depression patients, we demonstrate the model’s practical application. This new distribution fits the data better than competing models, supported by lower statistical metrics and likelihood ratio tests. Furthermore, the variables’ “simplicity” and “fatalism” significantly influence observed depression levels.

This study proposes a Bayesian regression model to explain soybean productivity by integrating vegetation indices, agronomic factors, environmental variables, and the effects of microbial seed inoculation. Field data were collected across multiple locations and growing seasons in Brazil, enabling the assessment of spatial and temporal variability. Microbial inoculation was performed using three Bacillus-based strains (1A11, 5D5, and 6E9), selected for their plant growth-promoting potential, including mechanisms such as enhanced nutrient availability, stimulation of root development, and increased tolerance to abiotic stress. These strains were applied as liquid formulations to soybean seeds and evaluated under field conditions in comparison with a non-inoculated control. The model included block effects, cultivars, inoculation treatments, accumulated precipitation, and spectral indices (Enhanced Vegetation Index 2 - EVI2 and Normalized Difference Vegetation Index - NDVI). Inference was performed using Markov Chain Monte Carlo (MCMC) sampling with the No-U-Turn Sampler (NUTS), enabling uncertainty quantification. The results showed better predictive performance for EVI2 than for NDVI. Microbial inoculation consistently contributed to yield gains, while cultivar-specific responses were also observed, with stronger effects in longer maturity groups. The proposed model showed good fit, stability, and predictive capacity. It is concluded that the integrated Bayesian approach is efficient for modeling soybean productivity under multiple explanatory variables.

Sugarcane ( Saccharum officinarum L.) is one of the largest crops in Brazil, and its productivity varies according to the environment and management practices adopted. In this study, tons of sugar per hectare (TSH) are estimated using a heteroscedastic gamma (GA) regression model, which considers several explanatory variables, one of which is the normalized difference green vegetation index (GNDVI), obtained from multispectral images in two locations over two consecutive growing seasons. The modeling considers regression structures in the parameters representing the mean and coefficient of variation, respectively. The results show that there is an influence of location, cultivar, cycle, accumulated precipitation, and GNDVI. To verify if the model is well-fitted to the data, the analysis of quantile residuals shows that the model is adequate. Therefore, the results indicate that heteroscedastic GA regression is an alternative model for predicting TSH and can assist in decision-making in sugarcane cultivation.

Greening, or Huanglongbing (HLB), poses a severe threat to global citrus cultivation, affecting various citrus species and compromising fruit production. Primarily transmitted by psyllids during phloem feeding, the bacterium Candidatus Liberibacter induces detrimental symptoms, including leaf yellowing and reduced fruit quality. Given the limitations of conventional control strategies, the search for innovative approaches, such as resistant genotypes and early diagnostic methods, becomes essential for the sustainability of citrus cultivation. The development of predictive models, such as the one proposed in this study, is essential as it enables the estimation of the bacterium's concentration and the vulnerability of healthy plants to infection, which will be instrumental in determining the risk of HLB. This study proposes a prediction model utilizing environmental factors, including temperature, humidity, and precipitation, which play a decisive role in greening epidemiology, influencing the complex interaction among the pathogen, vector, and host plant. In the proposed modeling, it addresses non-linear relationships through cubic smoothing splines applications and tackles imbalanced categorical predictor variables, requiring the use of a random-effects regression model, incorporating a random intercept to account for variability across different groups and mitigate the risk of biased predictions. The model's ability to predict HLB incidence under varying climatic conditions provides a significant contribution to disease management, offering a strategic tool for early intervention and potentially reducing the spread of HLB. Using climatological and environmental data, the research aims to develop a predictive model, assessing the influence of these variables on the spread of Candidatus Liberibacter asiaticus, essential for effective disease management. The proposed flexible model demonstrates robust predictions for both training and test data, identifying climatological and environmental predictors influencing the dissemination of Candidatus Liberibacter asiaticus, the vascular bacterium associated with Huanglongbing (HLB) or greening.

Grain producers in Brazil often depend on third-party services for the transportation, processing and storage of their production, as, for the most part, they do not have silos on their properties. In this context, efficient logistics is essential to optimize processes and increase reliability between customers and service providers. This study focuses on the logistical analysis of truck traffic at two grain processing plants, examining different receiving protocols to evaluate internal vehicle flow during peak production conditions. The data is analyzed using a multiple regression model with two systematic components based on the proposed New Weibull inverse Gaussian distribution. The research is conducted in grain processing and storage units in the southwest region of São Paulo-SP, belonging to an agro-industrial cooperative. The study monitors all stages of soybean receipt during the peak harvest month, in March 2020. The results indicate the dependence of service times on the sector's logistical variables. This research addresses the pressing need for efficient logistics in the grain industry, especially in soybean processing. By focusing on truck traffic and receiving protocols, the study aims to provide a better understanding to optimize internal logistics processes, thus contributing to improving operational efficiency and customer service in grain processing units.

This research explores the estimation of sugarcane (Saccharum officinarum L.) productivity at harvest by leveraging average satellite image time series collected during the growth phase. The study aims to evaluate the effectiveness of diverse modeling approaches, including a Heteroskedastic Gamma Regression model, Random Forest, and Neural Networks, in predicting sugarcane yield using satellite-derived vegetation indices and environmental variables. Key covariates such as sugarcane varieties, production cycle, accumulated precipitation during the growth phase, and the mean GNDVI vegetation index during the growth phase were analyzed in two different locations for two consecutive growth seasons. The research highlights the integration of satellite data with advanced statistical and machine learning techniques to enhance productivity forecasting in agricultural systems, with a focus on sugarcane cultivation. Additionally, the Heteroskedastic Gamma Regression model demonstrated superior performance in explaining productivity variability, particularly in commercial sugarcane fields, achieving an R2 of 0.89. The findings underscore the potential of these models to support informed decision-making and optimize agricultural practices, offering valuable insights for precision farming.

Several statistical models have been proposed in recent years, among them is the semiparametric regression. In medicine, there are several situations in which it is impracticable to consider a linear regression for statistical modeling, especially when the data contain explanatory variables that present a nonlinear relationship with the response variable. Another common situation is when the response variable does not have a unimodal shape, and it is not possible to adopt distributions belonging to the symmetric or asymmetric classes. In this context, a semiparametric heteroskedastic regression is proposed based on an extension of the normal distribution. Then, we show the usefulness of this model to analyze the cost of prostate cancer surgery. The predictor variables refer to two groups of patients such that one group receives a multimodal local anesthetic solution (Preemptive Target Anesthetic Solution) and the second group is treated with neuraxial blockade (spinal anesthesia/traditional standard). The other relevant predictor variables are also evaluated, thus allowing for the in-depth interpretation of the predictor variables with a nonlinear effect on the dependent variable cost. The penalized maximum likelihood method is adopted to estimate the model parameters. The new regression is a useful statistical tool for analyzing medical data.

In this paper, we propose a new regression model based on an extension of the Rice distribution to model linear and nonlinear effects for correlated data in the presence of bimodality. The new model is referred to as the odd log-logistic Rice distribution and we provide general mathematical properties, including the event risk and moments. We discuss parameter estimation by the penalized maximum likelihood method. We also present several simulations with different parameter configurations and sample sizes to analyze the behavior of the maximum likelihood estimators, as well as to study the empirical distribution of the quantile residuals. The usefulness of the proposed regression model is proved empirically through analysis of a real dataset.