scholarly journals MODELAGEM NÃO LINEAR DO CRESCIMENTO EM ALTURA DO CAFEEIRO IRRIGADO E NÃO IRRIGADO EM DIFERENTES DENSIDADES

Irriga ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 140
Author(s):  
Adriele Aparecida Pereira ◽  
Tales Jesus Fernandes ◽  
Myriane Stella Scalco ◽  
Augusto Ramalho De Morais
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Gompertz Model ◽  
Generalized Least Squares ◽  
Residual Variance ◽  
Growth Data ◽  
Nonlinear Growth ◽  
Heterogeneity Of Variance ◽  
E Mail ◽  
Over Time

MODELAGEM NÃO LINEAR DO CRESCIMENTO EM ALTURA DO CAFEEIRO IRRIGADO E NÃO IRRIGADO EM DIFERENTES DENSIDADES  ADRIELE APARECIDA PEREIRA1; TALES JESUS FERNANDES2; MYRIANE STELLA SCALCO3 E AUGUSTO RAMALHO DE MORAIS4 1Licenciada em Matemática, Mestre, DEX/UFLA, Lavras-MG, e-mail: [email protected] em Matemática, Doutor, Prof. DEX/UFLA, Lavras-MG, e-mail: [email protected] Agrônoma, Doutora, DAG/UFLA, Lavras-MG, e-mail: [email protected] Agrônomo, Doutor, Prof. DEX/UFLA, Lavras-MG, e-mail: [email protected]  1 RESUMO Heterogeneidade de variâncias e autocorrelação residual são características inerentes à dados de crescimento ao longo do tempo que se não considerados nas análises podem conduzir a resultados imprecisos. Este estudo teve por objetivo comparar os ajustes dos modelos Logístico e Gompertz, considerando os métodos de mínimos quadrados: ordinários e generalizados. Os dados utilizados referem-se à altura de plantas do cafeeiro, submetidas aos regimes de irrigação Si (testemunha), 60 kPa e 140 kPa, nas densidades de plantio 2500 e 5000 plantas ha-1. Segundo o desvio padrão residual e a análise de resíduos, o ajuste do modelo Gompertz pelo método de mínimos quadrados generalizados, que incorpora a heterogeneidade de variâncias e autocorrelação residual na modelagem, apresentou os melhores resultados para todos os dados analisados, sendo indicado para modelar o crescimento em altura do cafeeiro ao longo do tempo. Os ajustes referentes às plantas irrigadas apresentaram as maiores estimativas para a altura assintótica, confirmando que a irrigação da lavoura proporciona maior crescimento das plantas. Palavras-Chave: Autocorrelação residual, Gompertz, Heterocedasticidade.  PEREIRA, A. A.; FERNANDES, T. J.; SCALCO, M. S.; MORAIS, A. R. de MODELING NONLINEAR GROWTH IN HEIGHT COFFEE WITH AND WITHOUT IRRIGATION IN DIFFERENT DENSITIES  2 ABSTRACT Heterogeneity of variance and residual autocorrelation characteristics are inherent in the growth data over time that is not considered in the analysis may lead to inaccurate results. This study aimed to compare the settings of the Logistic and Gompertz models, considering the methods of least squares: ordinary and generalized. The data used refer to the height of the coffee plants, subjected to irrigation systems Si (non irrigated), 60 kPa and 140 kPa, the planting densities in 2500 and 5000 plants ha-1. According to the residual standard deviation and the residual analysis, the fit of the Gompertz model by generalized least squares method, which incorporates the heterogeneity of residual variance and autocorrelation in modeling, showed the best results for all data analyzed, suitable for modeling the growth in height of the coffee over time. The adjustments related to the irrigated plants had the highest estimates for the asymptotic height, confirming that the crop irrigation provides greater plant growth. Keywords: Residual autocorrelation, Gompertz, Heteroscedasticity.

Spatiotemporal palaeodiversity patterns of modern crocodiles (Crocodyliformes: Eusuchia)

2019 ◽  
Vol 189 (2) ◽  
pp. 635-656 ◽  
Author(s):  
Ane De Celis ◽  
Iván Narváez ◽  
Francisco Ortega
Keyword(s):  
South America ◽  
Least Squares ◽  
Late Miocene ◽  
North American ◽  
Fossil Record ◽  
Generalized Least Squares ◽  
Global Scale ◽  
Biotic Factors ◽  
Species Dynamics ◽  
Over Time

Abstract Eusuchia is a crocodyliform clade with a rich and diverse fossil record dating back to the Mesozoic. There are several recent studies that analyse crocodyliform palaeodiversity over time, but none of them focuses exclusively on eusuchians. Thus, we estimated subsampled eusuchian palaeodiversity species dynamics over time not only at a global scale, but also by continents and main crocodylian lineages (Alligatoroidea, Crocodyloidea and Gavialoidea). These estimates reveal complex spatiotemporal palaeodiversity patterns, in which two maxima can be detected: the first during the Palaeocene and the second, which is also the biggest, in the middle-late Miocene. The Palaeocene shift is related to a North American alligatoroid diversification, whereas the middle–late Miocene maximum is related to a diversification of the three main Crocodylia lineages in Gondwanan land masses, but especially in South America. Additionally, a model-based study using generalized least squares was carried out to analyse the relationships between different abiotic and sampling proxies and eusuchian palaeodiversity. The results show that palaeotemperature is the most important factor amongst the analysed proxies, in accordance with previous studies. However, the results suggest that, along with palaeotemperature, other abiotic and/or biotic factors might also be driving eusuchian palaeodiversity dynamics.

Estimating an Autoregressive Current Effects Model of Sales Response when Observations are Aggregated over Time: Least Squares versus Maximum Likelihood

1988 ◽  
Vol 25 (3) ◽  
pp. 301-307
Author(s):  
Wilfried R. Vanhonacker
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Serial Correlation ◽  
Mean Squared Error ◽  
Generalized Least Squares ◽  
Parameter Estimates ◽  
Squared Error ◽  
Positive Serial Correlation ◽  
Serial Correlation Coefficient ◽  
Over Time

Estimating autoregressive current effects models is not straightforward when observations are aggregated over time. The author evaluates a familiar iterative generalized least squares (IGLS) approach and contrasts it to a maximum likelihood (ML) approach. Analytic and numerical results suggest that (1) IGLS and ML provide good estimates for the response parameters in instances of positive serial correlation, (2) ML provides superior (in mean squared error) estimates for the serial correlation coefficient, and (3) IGLS might have difficulty in deriving parameter estimates in instances of negative serial correlation.

Harmonizing biomass tables by generalized least squares

1985 ◽  
Vol 15 (2) ◽  
pp. 331-340 ◽  
Author(s):  
T. Cunia ◽  
R. D. Briggs
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Weighted Least Squares ◽  
Regression Function ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
Sample Tree ◽  
Tree Data ◽  
Regression Techniques ◽  
Ordinary Least Squares Method

To construct biomass tables for various tree components that are consistent with each other, one may use linear regression techniques with dummy variables. When the biomass of these components is measured on the same sample trees, one should also use the generalized rather than ordinary least squares method. A procedure is shown which allows the estimation of the covariance matrix of the sample biomass values and circumvents the problem of storing and inverting large covariance matrices. Applied to 20 sets of sample tree data, the generalized least squares regressions generated estimates which, on the average were slightly higher (about 1%) than the sample data. The confidence and prediction bands about the regression function were wider, sometimes considerably wider than those estimated by the ordinary weighted least squares.

The least-squares intersection of a family of lines and its application to phase equilibria

1982 ◽  
Vol 60 (15) ◽  
pp. 1978-1981 ◽  
Author(s):  
John W. Lorimer
Keyword(s):  
Phase Equilibria ◽  
Least Squares ◽  
Least Squares Method ◽  
Solid Phase ◽  
Generalized Least Squares ◽  
First Approximation ◽  
Generalized Least Squares Method ◽  
Point Of Intersection ◽  
Straight Lines ◽  
General Method

A generalized least-squares method is described for finding the point of intersection of a family of straight lines, each of which is defined by two experimental points. It is shown that the method of the least-squares triangle (Can. J. Chem. 59, 3076 (1981)) is a good first approximation to the general method. An example demonstrates the method of iteration of both parameters and observations for a problem involving evaluation of solid phase compositions from solubility measurements.

Generalized Least Squares Method, Heteroskedasticity, and Multivariate Regression

2012 ◽  
pp. 179-212
Author(s):  
Jean-Pierre Florens ◽  
Velayoudom Marimoutou ◽  
Anne Peguin-Feissolle ◽  
Josef Perktold ◽  
Marine Carrasco
Keyword(s):  
Least Squares ◽  
Multivariate Regression ◽  
Least Squares Method ◽  
Generalized Least Squares ◽  
Generalized Least Squares Method
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