You are not currently logged in.
Access JSTOR through your library or other institution:
Transformations in Density Estimation
M. P. Wand, J. S. Marron and D. Ruppert
Journal of the American Statistical Association
Vol. 86, No. 414 (Jun., 1991), pp. 343-353
Stable URL: http://www.jstor.org/stable/2290569
Page Count: 11
Preview not available
For the density estimation problem the global window width kernel density estimator does not perform well when the underlying density has features that require different amounts of smoothing at different locations. In this article we propose to transform the data with the intention that a global window width is more appropriate for the density of the transformed data. The density estimate of the original data is the "back-transform" by change of variables of the global window width estimate of the transformed data's density. We explore choosing the transformation from suitable parametric families. Data-based selection rules for the choice of transformations and the window width are discussed. Application to real and simulated data demonstrates the usefulness of our proposals.
Journal of the American Statistical Association © 1991 American Statistical Association