Curvas de índices de local em povoamentos de eucalipto obtidas por regressão quantílica

Abstract

Resumo -O objetivo deste trabalho foi obter curvas de índice de local, em povoamentos de eucalipto, por meio de regressão quantílica, e compará-las às obtidas com o uso de regressões ajustadas pelo método de mínimos quadrados ordinários. A regressão quantílica pode ser utilizada para a geração de curvas de índice de local, especialmente em presença de dados discrepantes. O método gera um feixe de curvas mais bem ajustadas aos dados observados, em comparação ao feixe gerado com as estimativas de mínimos quadrados ordinários, seja pelo método da curva-guia (curvas anamórficas), seja pela geração de curvas polimórficas (com o uso de diferentes percentis).Termos para indexação: Eucalyptus, dados discrepantes, método da curva-guia, mínimos quadrados ordinários, percentis. Site index curves in eucalyptus plantation obtained by quantile regressionAbstract -The objective of this work was to obtain site index curves in eucalyptus plantations by quantile regression, and to compare them to the obtained ones with the use of fitted regressions by the method of ordinary least squares. Quantile regression can be used to generate site index curves, especially in the presence of outliers. The method generates a bundle of curves better adjusted to the observed data, compared to the one generated with the estimates of ordinary least squares, either by the guide curve method (anamorphic curves) or by polymorphic curves (using different percentiles).

Artificial neural networks, quantile regression, and linear regression for site index prediction in the presence of outliers

“…methods differed in the database with outliers, but were similar in the one without them. This result is due to the fact that the LR is less tolerant to the presence of noise than the QR (Araújo Júnior et al, 2016), mainly when the discrepant values promote a change in average values, but interfere little in the median, as was the case for some dominant height classes (Table 2). Graphically, it is possible to observe a discrepancy between the curves for the values estimated by the LR and QR only when there are outliers in the database (Figure 2).…”

“…where H is the dominant height (meters); A is the age of the trees at measurement (months); β 0 and β 1 are parameters; Ln is the natural logarithm; and ε is the random error, with ε ~ NID (0, σ 2 ). For Schumacher's model, the adjustment was done with the ordinary least square method and with the minimization of the absolute error for the QR (Araújo Júnior et al, 2016), using the following equations:…”

“…It was firstly described by McCulloch & Pitts (1943) and has been considered as an excellent alternative to the traditional regression models. Both the QR and ANN have already been used with success to fit dominant height data, with focus on site index estimates (Cosenza et al, 2015;Araújo Júnior et al, 2016); however, these studies did not consider the presence of outliers in the database.…”

Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States

“…Linearization of biomass models has been used in many studies (Repola, 2008;Litton & Boone, 2008;Mugasha et al, 2013;Chave et al, 2014), mainly due to its simplicity (Ferraz Filho et al, 2018). Alternatively, other approaches can be used to increase the accuracy of growth predictions of forest stands such as Artificial Neural Networks (Binoti et al, 2013;Reis et al, 2016;Ferraz Filho et al, 2018), support vector machines (Cordeiro et al, 2015;Cosenza et al, 2015) and Quantile Regression (Araújo et al, 2016). However, these methods are computationally intensive and should be used with caution due to potential restrictions on extrapolations (Ferraz Filho et al, 2018).…”