Willian Luis de Oliveira

@des.uem.br

Assistant Professor, Department of Statistics / State University of Maringá
State University of Maringá (UEM)

Willian Luis de Oliveira


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https://researchid.co/wloliveira

RESEARCH, TEACHING, or OTHER INTERESTS

Statistics and Probability, Statistics, Probability and Uncertainty, Modeling and Simulation
3

Scopus Publications

Scopus Publications

  • Nanographite and graphene oxide: a comparative optimization of nanoreinforcement dosage in concrete with marble residue
    Pâmela Herrera Dutra, Maria Eliana Camargo Ferreira, Luiz Fernando Belchior Ribeiro, Adelina Pinheiro Santos, Willian Luís de Oliveira, Romel Dias Vanderlei, Natália Ueda Yamaguchi
    Journal of Sustainable Cement Based Materials, 2026
  • Huanglongbing vector insect counting (HLB) by GAMLSS
    Mateus Silva Pedroso, Terezinha Aparecida Guedes, Willian Luís de Oliveira, Werica Bruna da Silva Valim, William Mario de Carvalho Nunes, Vanderly Janeiro
    Acta Scientiarum Technology, 2024
    Citriculture is one of the most important agricultural activities globally, with Brazil being one of the leading world producers. Thus, such activity is essential for the country's economy and the producers who depend on it. In this sense, the fight against Huanglongbing, one of the most devastating citrus diseases caused by vector insects, is essential to guarantee the quality of the fruit and avoid economic losses. The present work analyzed the counting of insect vectors in a commercial orange orchard in an observational study carried out in the municipality of Paranavaí, state of Paraná, Brazil, using the methodologies of generalized linear mixed models (GLMM) and generalized additive models for location, scale, and form (GAMLSS), with Negative Binomial probability distribution. Data were obtained by counting insects trapped in sticky traps at twelve fixed points in the orchard at three different heights and collected over seven fortnights. The results indicated that the GAMLSS model presented better results by including the linear predictor for modeling the scale parameter associated with the study factors based on the AIC criterion and diagnostic analysis tools.
  • A class of bivariate regression models for discrete and/or continuous responses
    Willian Luís de Oliveira, Carlos Alberto Ribeiro Diniz, Maria Durbán
    Communications in Statistics Simulation and Computation, 2019
    A general class of models for discrete and/or continuous responses is proposed in which joint distributions are constructed via the conditional approach. It is assumed that the distributions of one response and of the other response given the first one belong to exponential family of distributions. Furthermore, the marginal means are related to the covariates by link functions and a dependency structure between the responses is inserted into the model. Estimation methods, diagnostic analysis and a simulation study considering a Bernoulli-exponential model, a particular case of the class, are presented. Finally, this model is used in a real data set.