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Considering model uncertainty in random regression models by means of Bayesian variable selection.
| Content Provider | Semantic Scholar |
|---|---|
| Author | Steibel, Juan P. Grignola, F. E. |
| Copyright Year | 2002 |
| Abstract | J.P. Steibel and F. Grignola 1 Facultad de AgronomÃa, Universidad de Buenos Aires, Cap. Fed., Argentina 2 Monsanto Company, Saint Louis, MO, USA INTRODUCTION In animal breeding, many traits admit repeated measurements or test-day records over time. In order to model the expectation and covariance of these characters as a function of time, the interest in using test-day models (TDM) in the context of the Gaussian mixed linear model has increased in recent years. Even though the advantages of TDM are well known, a problem that frequently arises is the choice of suitable number terms in the linear function. Jensen (2001) discussed various strategies about model choice and, within the Bayesian framework, he suggested Bayesian model averaging to consider the uncertainty around the model for the prediction of breeding values. In this work we implement a simple and flexible Bayesian approach proposed by Kuo and Mallick (1998) to subset a pre-specified set of covariates that best describe the trait of interest in a random coefficient regression model (RRM). The posterior probability of each regressor entering the model is computed using the Gibbs sampling algorithm. The method is illustrated with a simple example where the variable selection strategy is limited to the fixed effects. METHODS The method of Kuo and Mallick (1998) expands the usual regression model to include an indicator variable for each predictor considered. In standard notation and for a RRM, |
| Starting Page | 1 |
| Ending Page | 4 |
| Page Count | 4 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.wcgalp.org/system/files/proceedings/2002/considering-model-uncertainty-random-regression-models-means-bayesian-variable-selection.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
