Análise bayesiana do modelo fatorial dinâmico para um vetor de séries temporais utilizando a distribuição t multivariada

The multivariate t models are symmetric and with heavier tail than the normal distribution, important feature in financial data. In this theses is presented the Bayesian estimation of a dynamic factor model, where the factors follow a multivariate autoregressive model, using multivariate t distribution. Since the multivariate t distribution is complex, it was represented in this work as a mix between a multivariate normal distribution and a square root of a chi-square distribution. This method allowed to define the posteriors. The inference on the parameters was made taking a sample of the posterior distribution, through the Gibbs Sampler. The convergence was verified through graphical analysis and the convergence tests Geweke (1992) and Raftery & Lewis (1992a). The method was applied in simulated data and in the indexes of the major stock exchanges in the world.

Spatial distribution of Yellow Sigatoka Leaf Spot correlated with soil fertility and plant nutrition

This study analyzed the spatial distribution of Yellow Sigatoka Leaf Spot relative to soil fertility and plant nutritional status using geostatistics. The experimental area comprised 1.2 ha, where 27 points were georeferenced and spaced on a regular grid 18 × 18 m. The severity of Yellow Sigatoka, soil fertility and plant nutritional status were evaluated at each point. The spherical model was adjusted for all variables using restricted maximum likelihood. Kriging maps showed the highest infection rate of Sigatoka occurred in high areas of the field which had the highest concentration of sand, while the lowest disease was found in lower areas with lower silt, organic matter, total exchangeable bases, effective cation exchange capacity, base saturation, Ca and Mg in soil, and foliar sulfur (S). These results may help farmers manage Yellow Sigatoka disease more effectively, with balanced fertilization and reduced fungicide application. This practice minimizes the environmental impact and cost of production while contributing to production sustainability.