Open AccessNONLINEAR MODELS BASED ON QUANTILES IN THE FITTING OF GROWTH CURVES OF PEPPER GENOTYPES
Author(s) -
Ana Carolina Ribeiro de Oliveira,
Paulo R. Cecon,
Guilherme Alves Puiatti,
Maria Eduarda da Silva Guimarães,
Cosme Damião Cruz,
Fernando Luíz Finger,
Moysés Nascimento,
Mário Puiatti,
Maurício Silva Lacerda
Publication year - 2021
Publication title -
revista de matemática e estatística
Language(s) - English
Resource type - Journals
eISSN - 1980-4245
pISSN - 0102-0811
DOI - 10.28951/rbb.v39i3.505
Subject(s) - gompertz function , mathematics , akaike information criterion , statistics , pepper , nonlinear regression , ordinary least squares , logistic regression , residual sum of squares , quantile , non linear least squares , regression analysis , explained sum of squares , horticulture , biology
This study aimed to fit nonlinear regression models to model the growth of the characters fruit length (FL) and fruit width (FW) of pepper genotypes (Capsicum annuum L.) over time using the method of ordinary least squares (OLS); and identify the model with the best fit and compare it to the model obtained via nonlinear quantile regression (QR) in the 0.25, 0.5, and 0.75 quantiles. Three regression models (Logistic, Gompertz, and von Bertalanffy) and four fit quality evaluators were adopted: Akaike information criterion, residual mean absolute deviation, and parametric and intrinsic curvature measurements. Five commercial genotypes of pepper were evaluated. Characters FL and FW were evaluated weekly from seven days after flowering, totaling ten measurements. In the estimation by OLS, the Logistic and von Bertalanffy models were considered adequate according to the quality evaluators. In the comparison between the models above by OLS and QR, the superiority of models obtained by QR was verified for the character FL. For the character FW, QR was efficient in three out of the five genotypes, being a valuable alternative in the study of fruit growth.