Based on a random sample of size n from an unknown d-dimensional density f, the problem of selecting the variable (or adaptive) bandwidth in kernel estimation of f is investigated. The common strategy is to express the variable bandwidth at each observation as the product of a local bandwidth factor and a global smoothing parameter. For selecting the local bandwidth factor a method based on cluster analysis is proposed. This method is direct and intuitively appealing. For selecting the global smoothing parameter a method that is an adaptation of the frequency domain approach of selecting the fixed bandwidth in Wu and Tsai [2004. Root n bandwidths selectors in multivariate kernel density estimation. Probab. Theory Related Fields 129, 537-558] is used. For d = 1 and 2, extensive simulation studies have been done to compare the performance of our selector with the selectors of Abramson [1982. On bandwidth variation in kernel estimates-a square root law. Ann. Statist. 10, 1217-1223] and Sain and Scott [1996. On locally adaptive density estimation. J. Amer. Statist. Assoc. 91, 1525-1534] and Sain [2002. Multivariate locally adaptive density estimation. Comput. Statist. Data Anal. 39, 165-186], and the excellent performance of our selector at practical sample sizes is clearly demonstrated.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Statistics, Probability and Uncertainty