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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
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