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	Provedor de dados Rev. Ciênc. Agron. (1) AgEcon (1) Rev. Bras. Ciênc. Avic. (1) Autor Babcock, Bruce A. (1) Başar,EK (1) Fernandes,Tales Jesus (1) Firat,MZ (1) Karaman,E (1) Muniz,Joel Augusto (1) Narinc,D (1) Prado,Thalita Kelen Leal do (1) Savian,Taciana Villela (1) Yu, Tian (1) Mais... Palavra-chave Non-linear (3) Autoregressive errors (1) Bayesian (1) Bayesian estimation (1) Crop Production/Industries (1) Gibbs sampler (1) Gompertz (1) Hierarchical structure (1) Least squares (1) Logistic (1) Metropolis-Hastings algorithm (1) Production Economics (1) Regression (1) Richards (1) Risk and Uncertainty (1) Mais... Tipo do documento Info:eu-repo/semantics/article (2) Working or Discussion Paper (1) Ano 2020 (1) 2016 (1) 2011 (1) País Brazil (2) United States (1) Idioma Inglês (3)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 3 Primeira ... 1 ... Última Estimating Non-linear Weather Impacts on Corn Yield—A Bayesian Approach AgEcon Yu, Tian; Babcock, Bruce A.. We estimate impacts of rainfall and temperature on corn yields by fitting a linear spline model with endogenous thresholds. Using Gibbs sampling and the Metropolis - Hastings algorithm, we simultaneously estimate the thresholds and other model parameters. A hierarchical structure is applied to capture county-specific factors determining corn yields. Results indicate that impacts of both rainfall and temperature are nonlinear and asymmetric in most states. Yield is concave in both weather variables. Corn yield decreases significantly when temperature increases beyond a certain threshold, and when the amount of rainfall decreases below a certain threshold. Flooding is another source of yield loss in some states. A moderate amount of heat is beneficial to... Tipo: Working or Discussion Paper Palavras-chave: Bayesian estimation; Gibbs sampler; Hierarchical structure; Metropolis-Hastings algorithm; Non-linear; Crop Production/Industries; Production Economics; Risk and Uncertainty. Ano: 2011 URL: http://purl.umn.edu/103915 Bayesian Analysis for the Comparison of Nonlinear Regression Model Parameters: an Application to the Growth of Japanese Quail Rev. Bras. Ciênc. Avic. Firat,MZ; Karaman,E; Başar,EK; Narinc,D. ABSTRACT This paper discusses the Bayesian approach as an alternative to the classical analysis of nonlinear models for growth curve data in Japanese quail. A Bayesian nonlinear modeling method is introduced and compared with the classical nonlinear least squares (NLS) method using three non-linear models that are widely used in modeling the growth data of poultry. The Gompertz, Richards and Logistic models were fitted to 499 Japanese quail weekly averaged body weight data. Normal prior was assumed for all growth curve parameters of the models with assuming Jeffreys' non-informative prior for residual variances. Models were compared based on the Bayesian measure of fit, deviance information criterion (DIC), and our results indicated the better fit of... Tipo: Info:eu-repo/semantics/article Palavras-chave: Gompertz; Logistic; Richards; Non-linear; Bayesian. Ano: 2016 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-635X2016000500019 Study on the growth curve of the internal cavity of ‘Dwarf green’ coconut fruits Rev. Ciênc. Agron. Prado,Thalita Kelen Leal do; Savian,Taciana Villela; Fernandes,Tales Jesus; Muniz,Joel Augusto. ABSTRACT The aim of the work was to evaluate the adjustment of the logistic and gompertz model with structure of first-order autoregressive errors in the study on the ‘Dwarf green’ coconut fruit growth based on longitudinal and cross-sectional internal cavity diameter data (DLCI and DTCI). Model adjustments showed positive residual autocorrelation, according to the Durbin-Watson test and for both variables, DLCI and DTCI, the residue was modeled according to first-order autoregressive process (AR1). The analysis was performed using the least squares method in the PROC MODEL of the SAS software and results indicated that for both characteristics under study, the logistic model was the most appropriate in describing fruit growth and, according to the model,... Tipo: Info:eu-repo/semantics/article Palavras-chave: Regression; Least squares; Autoregressive errors; Non-linear. Ano: 2020 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902020000300401 Registros recuperados: 3 Primeira ... 1 ... Última

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