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Statistical inference on transformation models: a self-induced smoothing approach.
- Source :
-
Journal of Nonparametric Statistics . Jun2018, Vol. 30 Issue 2, p308-331. 24p. - Publication Year :
- 2018
-
Abstract
- This paper deals with a general class of transformation models that contains many important semiparametric regression models as special cases. It develops a self-induced smoothing for the maximum rank correlation estimator, resulting in simultaneous point and variance estimation. The self-induced smoothing does not require bandwidth selection, yet provides the right amount of smoothness so that the estimator is asymptotically normal with mean zero (unbiased) and variance-covariance matrix consistently estimated by the usual sandwich-type estimator. An iterative algorithm is given for the variance estimation and shown to numerically converge to a consistent limiting variance estimator. The approach is applied to a data set involving survival times of primary biliary cirrhosis patients. Simulation results are reported, showing that the new method performs well under a variety of scenarios. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10485252
- Volume :
- 30
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Journal of Nonparametric Statistics
- Publication Type :
- Academic Journal
- Accession number :
- 129301647
- Full Text :
- https://doi.org/10.1080/10485252.2018.1424334