General partially linear varying-coefficient transformation models for ranking data

Abstract

In this paper,we propose a class of general partially linear varying-coefficient transformation models for ranking data. In the models, the functional coefficients are viewed as nuisance parameters and approximated by B-spline smoothing approximation technique. The B-spline coefficients and regression parameters are estimated by rank-based maximum marginal likelihood method. The three-stage Monte Carlo Markov Chain stochastic approximation algorithm based on ranking data is used to compute estimates and the corresponding variances for all the B-spline coefficients and regression parameters. Through three simulation studies and a Hong Kong horse racing data application, the proposed procedure is illustrated to be accurate, stable and practical.

Keywords:

• general partially linear varying-coefficient transformation models
• marginal likelihood
• B-spline

AMS 2000 Subject Classification :

• Primary: 62G08
• Secondary: 62E20

Acknowledgements

We are grateful to the editor, associate editor and referees for their helpful comments which led to the revised version of this paper. This research was partially supported by PhD Teacher's Research Support Project Foundation of Xuzhou Normal University (11XLR31) and Specialized Research Fund for the Doctoral Program of Higher Education of China (20111108120002).