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General partially linear varying-coefficient transformation models for ranking data.
- Source :
-
Journal of Applied Statistics . Jul2012, Vol. 39 Issue 7, p1475-1488. 14p. 3 Charts, 3 Graphs. - Publication Year :
- 2012
-
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. [ABSTRACT FROM PUBLISHER]
- Subjects :
- *MARKOV processes
*MONTE Carlo method
*ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 02664763
- Volume :
- 39
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- Journal of Applied Statistics
- Publication Type :
- Academic Journal
- Accession number :
- 75908522
- Full Text :
- https://doi.org/10.1080/02664763.2012.658357