Name
Abstract | ||
|---|---|---|
Nested error regression models are useful tools for the analysis of grouped data, especially in the context of small area estimation. This paper suggests a nested error regression model using uncertain random effects in which the random effect in each area is expressed as a mixture of a normal distribution and a positive mass at0. For the estimation of the model parameters and prediction of the random effects, an objective Bayesian inference is proposed by setting non-informative prior distributions on the model parameters. Under mild sufficient conditions, it is shown that the posterior distribution is proper and the posterior variances are finite, confirming the validity of posterior inference. To generate samples from the posterior distribution, a Gibbs sampling method is provided with familiar forms for all the full conditional distributions. This paper also addresses the problem of predicting finite population means, and a sampling-based method is suggested to tackle this issue. Finally, the proposed model is compared with the conventional nested error regression model through simulation and empirical studies. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.jmva.2016.09.011 | J. Multivariate Analysis |
Keywords | Field | DocType |
62F12,62J05 | Econometrics,Random effects model,Normal distribution,Nested sampling algorithm,Bayesian inference,Bayesian linear regression,Posterior probability,Bayesian hierarchical modeling,Statistics,Gibbs sampling,Mathematics | Journal |
Volume | Issue | ISSN |
153 | C | 0047-259X |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shonosuke Sugasawa | 1 | 0 | 0.34 |
| Tatsuya Kubokawa | 2 | 36 | 11.73 |