General partially linear varying-coefficient transformation models for ranking data |
| |
Authors: | Jianbo Li Minggao Gu Tao Hu |
| |
Institution: | 1. School of Mathematical Sciences , Xuzhou Normal University , NO. 101, Shanghai Road, Xuzhou , Jiangsu , 221116 , People's Republic of China;2. Department of Statistics , The Chinese University of Hong Kong , Shatin , NT , Hong Kong;3. School of Mathematical Sciences , Capital Normal University , Beijing , 100045 , People's Republic of China |
| |
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 |
|
|