General partially linear varying-coefficient transformation models for ranking data

Jianbo Li, Minggao Gu and Tao Hu

Journal of Applied Statistics, 2012, vol. 39, issue 7, 1475-1488

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.

Date: 2012
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DOI: 10.1080/02664763.2012.658357

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