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| Belasco, Eric J.; Ghosh, Sujit K.. |
| 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... |
| Tipo: Conference Paper or Presentation |
Palavras-chave: Censoring; Livestock production; Tobit; Zero-inflated; Bayesian; Livestock Production/Industries. |
| Ano: 2008 |
URL: http://purl.umn.edu/6341 |
