Data/hora: 01/06/2024 04:41:10

Provedor de dados: 	31	
País: 	United States	
Título: 	Modeling Censored Data Using Mixture Regression Models with an Application to Cattle Production Yields	
Autores: 	Belasco,   Eric J.; Ghosh,   Sujit K.	
Data: 	2008-05-06	
Ano: 	2008	
Palavras-chave: 	Censoring; Livestock production; Tobit; Zero-inflated; Bayesian; Livestock Production/Industries.	
Resumo: 	This research develops a mixture regression model that is shown to have advantages over the classical Tobit model in model fit and predictive tests when data are generated from a two step process. Additionally, the model is shown to allow for flexibility in distributional assumptions while nesting the classic Tobit model. A simulated data set is utilized to assess the potential loss in efficiency from model misspecification, assuming the Tobit and a zero-inflated log-normal distribution, which is derived from the generalized mixture model. Results from simulations key on the finding that the proposed zero-inflated log-normal model clearly outperforms the Tobit model when data are generated from a two step process. When data are generated from a Tobit model, forecasts are more accurate when utilizing the Tobit model. However, the Tobit model will be shown to be a special case of the generalized mixture model. The empirical model is then applied to evaluating mortality rates in commercial cattle feedlots, both independently and as part of a system including other performance and health factors. This particular application is hypothesized to be more appropriate for the proposed model due to the high degree of censoring and skewed nature of mortality rates. The zero-inflated log-normal model clearly models and predicts with more accuracy that the tobit model.	
Tipo: 	Conference Paper or Presentation	
Idioma: 	Inglês	
Identificador: 	29969

http://purl.umn.edu/6341	
Relação: 	American Agricultural Economics Association&gt;2008 Annual Meeting, July 27-29, 2008, Orlando, Florida

Selected Paper

456273	
Formato: 	42