CV Lattes
ResearcherID
ORCID
Google Scholar Citations
Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Bachelor's degree in Statistics from the Federal University of Bahia (2002-2006), where I was a CNPq undergraduate research fellow and worked at the Institute of Collective Health (ISC/UFBA). Masters degree in Biometrics and Applied Statistics from the Federal Rural University of Pernambuco (2007-2008), funded by CAPES, with a dissertation developed in the area of Statistical and Computational Modeling, covering topics such as speciesarea relationships, species diversity, and stochastic and regression models. Ph.D. in Statistics from the Federal University of São Carlos (2009-2013), funded by FAPESP, with a thesis developed in the area of Inference and Regression Models, with a primary focus on regression models for count data. She completed a postdoctoral fellowship in the Department of Statistics at the University of Connecticut, Storrs, CT, USA (20202021). Currently, she serves as an Associate Professor in the Department of Applied Mathematics and Statistics at the Institute of Mathematics and Computer Science of the University of São Paulo, at the São Carlos campus. Research interests: Power Series Distributions; Regression Models for Count Data; Bayesian Inference; Computational Statistics; Applied Statistics. (Source: Lattes Curriculum)
| News published in Agência FAPESP Newsletter about the researcher |
| More itemsLess items |
| TITULO |
| Articles published in other media outlets ( ): |
| More itemsLess items |
| VEICULO: TITULO (DATA) |
| VEICULO: TITULO (DATA) |
This paper proposes a statistical generalized species-area model (GSAM) to represent various patterns of species-area relationship (SAR, which is one of the fundamental patterns in ecology. The approach enables the generalization of many preliminary models, as power-curve model, which is commonly used to mathematically describe the SAR. The GSAM is applied to simulated data set of species…
This work aims to evaluate the performance of a more flexible distribution (the Zero-Modified Negative Binomial Distribution) in scenarios where a simpler distribution, such as the Zero-Modified Poisson Distribution, is theoretically appropriate. Specifically, it seeks to investigate the performance of the Zero-Modified Negative Binomial in equidispersed dataset, that is, those originatin…
In this project, the cross-validation technique will be considered to select the explicative variables to be included in the regression model. Assuming that the variable of interest is a counting process, the Zero-Modified Poisson distribution, which is a generalization of the Poisson distribution, will be considered for the modeling approach, since in many practical situations there may …
In this project, we will consider some criteria that evaluate the goodness-of-fit of the k-modified regression models. The k-modification consists of the inclusion of a parameter in the probability mass function of the traditional discrete distributions, capable of modeling the inflation or deflation of the observation k in the data set. In this sense, the modification becomes essential w…
In this work, we present a family of distributions for count data, the so called Zero-Modified Power Series, which is an extension of the Power Series family of distributions whose support starts at zero. This extension consists in modifying the probability of observing zero of each Power Series distribution, allowing that the new zero-modified distribution being appropriately adjusted to…
Count datasets are common in many areas of knowledge and, consequently, more general discrete distributions have been proposed due to the unique characteristics of each set. In particular, count data may present discrepancies (greater or lesser) in the observed frequencies of two observations, said k1 and k2, by comparing them with their expected frequencies obtained from a particular dis…