Finite Mixture Regression Models for Stratified Sampling Design
Presentation Type
Poster
Student
Yes
Track
Methodology
Abstract
Despite the popularity and importance, there is limited work on modeling data, which come from complex survey design using finite mixture models. In this work, we explored the use of finite mixture regression models when the samples were drawn using a complex survey design. In particular, we considered modeling data collected based on stratified sampling design. We developed a new design-based inference where we integrated sampling weights in the complete-data log-likelihood function. The expectation-maximization algorithm was developed accordingly. A simulation study was conducted to compare the new methodology with the usual finite mixture of a regression model. The comparison was done using bias-variance components of mean square error. Additionally, a simulation study was conducted to assess the ability of the Bayesian information criterion to select the optimal number of components under the weighted modeling approach. The methodology was implemented on real data with good results.
Start Date
2-5-2019 12:00 PM
End Date
2-5-2019 1:00 PM
Finite Mixture Regression Models for Stratified Sampling Design
Volstorff A
Despite the popularity and importance, there is limited work on modeling data, which come from complex survey design using finite mixture models. In this work, we explored the use of finite mixture regression models when the samples were drawn using a complex survey design. In particular, we considered modeling data collected based on stratified sampling design. We developed a new design-based inference where we integrated sampling weights in the complete-data log-likelihood function. The expectation-maximization algorithm was developed accordingly. A simulation study was conducted to compare the new methodology with the usual finite mixture of a regression model. The comparison was done using bias-variance components of mean square error. Additionally, a simulation study was conducted to assess the ability of the Bayesian information criterion to select the optimal number of components under the weighted modeling approach. The methodology was implemented on real data with good results.