Fuzzy sets in semiparametric Bayes Regression | ||
| basrah journal of science | ||
| Article 1, Volume 32, Issue 1, February 2014, Pages 141-167 PDF (0 K) | ||
| Authors | ||
| Ameera Jaber Mohaisen; Ammar Muslim Abdulhussein | ||
| Abstract | ||
| In this paper, we consider semi parametric regression model where the mean function of this model has two part, the parametric ( first part ) is assumed to be linear function of p-dimensional covariates and nonparametric ( second part ) is assumed to be a smooth penalized spline. By using a convenient connection between penalized splines and mixed models, we can representation semi parametric regression model as mixed model. Bayesian approach to semi parametric regression is described using fuzzy sets and membership functions. The membership functions are interpretedas likelihood functions for the model. Bayesian approach is employed to making inferences on the resulting mixed model coefficients, and we prove some theorems about posterior and Bayes factor. | ||
| Keywords | ||
| Mixed models; semi parametric regression; Penalized spline; Fuzzy sets; Membership functions; Bayesian inference; Prior density; Posterior density; Bayesfactor | ||
|
Statistics Article View: 92 PDF Download: 57 |
||