Thiago G. Ramires

A new family of distributions with properties, regression models and applications

Abstract In this study, we introduce a new family of continuous distributions with one extra shape parameter, called the Burr-Hatke-G family, based on the Burr-Hatke differential equation. Some of its mathematical properties are derived. The maximum likelihood method is used to estimate the model parameters. Moreover, the log-Burr-Hatke-Weibull regression model based on new the generated family is introduced. The usefulness of the proposed family is demonstrated by means of the three real data applications. Empirical results indicate that the proposed family provides more realistic fits than other well-known family of distributions.

Predicting weed invasion in a sugarcane cultivar using multispectral image

ABSTRACT The cultivation of sugar cane has been gaining great focus in several countries due to its diversity of use. The modernization of agriculture has allowed high productivity, which is affected by the invasion of weeds. With sustainable agriculture, the use of herbicides has been increasingly avoided in society, requiring more effective weed control methods. In this paper, we propose a statistical model capable of identifying the invasion of weeds in the field, using four color spectra as regressor variables obtained by a multispectral camera mounted on an unmanned aerial vehicle. With the exact identification of the weed infestation, it is possible to carry out the management in the field with herbicide applications in the exact places, thus avoiding the increase of the cost of production or even dispensing with the use of herbicides, effecting the mechanical removal of them. Results show that in the experimental field, it was possible to reduce herbicide spraying by 57%.

The new family of distributions and applications in heteroscedastic regression analysis

First we introduce and study some general mathematical properties of a new generator of continuous distributions with two extra shape parameters called the odd generalized half-Cauchy family. A second goal, we introduce the new log-generalized odd half-Cauchy heteroscedastic regression model with censored data, which represents a parametric family of models that includes as sub-models several widely known regression models that can be applied to data with no homogeneity of variance. Maximum likelihood estimation of the model parameters with censored data as well as a simulation study are investigated. We also prove empirically the flexibility of the new family by means real data sets.