Nonparametric Estimation of the Transformation in the Transform-Both-Sides Regression Model
作者:
Naisyin Wang,
David Ruppert,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1995)
卷期:
Volume 90,
issue 430
页码: 522-534
ISSN:0162-1459
年代: 1995
DOI:10.1080/01621459.1995.10476544
出版商: Taylor & Francis Group
关键词: Kernel density estimator;Normalizing transformation;Rates of convergence;Regression transformation;Variance stabilization
数据来源: Taylor
摘要:
The transform-both-sides (TBS) regression model developed by Carroll and Ruppert is applicable when the relationship between the median response and the independent variables has been identified. Several different families of transformations, such as the Box-Cox power family, have been considered in the parametric approach to this model. In this article, we propose a nonparametric estimator of the transformation in the TBS model allowing general smooth monotonic transformations. Asymptotic properties of this estimator are discussed.
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