General partially linear varying-coefficient transformation models for ranking data
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: B-spline; Primary: 62G08; Secondary: 62E20; general partially linear varying-coefficient transformation models; marginal likelihood
Document Type: Research Article
Affiliations: 1: School of Mathematical Sciences,Xuzhou Normal University, NO. 101, Shanghai RoadXuzhou,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
Publication date: 01 July 2012
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