Regressão não linear quantílica para modelagem de dados de crescimento de milho

Pollyane Vieira da Silva; Taciana Villela Savian

doi:10.5433/1679-0375.2025.v46.53256

Quantile nonlinear regression for modeling maize growth data

Authors

Pollyane Vieira da Silva Universidade Federal de Pelotas https://orcid.org/0000-0002-5795-4943
Taciana Villela Savian Universidade de São Paulo https://orcid.org/0000-0002-3309-6075

DOI:

https://doi.org/10.5433/1679-0375.2025.v46.53256

Keywords:

nonlinear models, quantile regression, intrinsec nonlinearity measure, corn culture, growth curve analisys

Abstract

Statistical modeling is fundamental in plant growth studies as it guides management practices across different developmental stages. Among existing approaches, nonlinear regression models stand out for describing growth patterns over time through estimated parameters, typically obtained via least squares methods. However, this approach is limited to average data analysis and remains sensitive to outliers and variance heterogeneity. Quantile regression emerges as a robust alternative, enabling estimation across different quantiles without requiring normality assumptions for errors. This study aimed to analyze corn plant growth over time using quantile nonlinear regression and to investigate critical points in the applied models. We evaluated logistic, Gompertz, and Chanter models, assessing their goodness-of-fit through the Akaike Information Criterion (AIC), intrinsic nonlinearity measures, and two novel statistics proposed here: quantile correlation and the weighted sum of squared deviations (WSSD). Results demonstrated that the Chanter model provided the best fit for the analyzed dataset, with strong performance across different quantiles.

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Author Biographies

Pollyane Vieira da Silva, Universidade Federal de Pelotas

Professor of Statistics at the Federal University of Pelotas (UFPel). She has experience in Statistics and Education. She earned a bachelor's degree in Mathematics from UNESP's Rio Claro campus (2011-2016) and a second degree in Pedagogy from UNIVESP (2018-2022). In 2018, she completed her master's degree and in 2022 her doctorate in Statistics and Agricultural Experimentation at ESALQ/USP, focusing on nonlinear models applied to animal and plant growth data. In 2021, she completed a specialization in School Management at the PECEGE Institute and an MBA at ESALQ/USP.

Taciana Villela Savian, Universidade de São Paulo

She holds a degree in Animal Science from the Federal University of Lavras (2002), a master's degree in Agronomy (Statistics and Agricultural Experimentation) from the Federal University of Lavras (2005), a PhD in Statistics and Agricultural Experimentation from the Federal University of Lavras (2008) and a post-doctorate (PRODOC) from the Federal University of Lavras (2010). She is currently a professor at the Luiz de Queiroz College of Agriculture at the University of São Paulo (USP). She has experience in Non-Linear Regression Models, working mainly on the following topics: non-linear regression, ruminal degradability model, animal and plant growth curves.

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