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Classified functional mixed effects model prediction

Abstract Number:

3883 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Xiaoyan Liu (1), Jiming Jiang (2)

Institutions:

(1) N/A, N/A, (2) University of California, Davis, N/A

Co-Author:

Jiming Jiang  
University of California, Davis

First Author:

Xiaoyan Liu  
N/A

Presenting Author:

Xiaoyan Liu  
N/A

Abstract Text:

In nowadays biomedical research, there has been a growing demand for making accurate predictions at subject levels. In many of these situations, data are collected as longitudinal curves and display distinct individual characteristics. Thus, prediction mechanisms accommodated with functional mixed effects models (FMEM) are useful. In this paper, we developed a classified functional mixed model prediction (CFMMP) method, which adapts classified mixed model prediction (CMMP) to the framework of FMEM. Performance of CFMMP against functional regression prediction based on simulation studies and the consistency property of CFMMP estimators are explored. Real-world applications of CFMMP are illustrated using real world examples including data from the hormone research menstrual cycles and the diffusion tensor imaging.

Keywords:

Classification|CMMP| functional mixed effects model|mean squared prediction error| |

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