论文信息 - A Modelling Approach for Bandwidth Selection in Kernel Density Estimation

A Modelling Approach for Bandwidth Selection in Kernel Density Estimation

A new procedure is proposed for bandwidth selection in univariate kernel density estimation. Rather than concentrate on minimising some criterion based upon the mean integrated square error (MISE), which depends directly on the (unknown) true density, we build a model for the data and use sampling methods to make inferences about the bandwidths. The model is Bayesian, and it is noted that it allows for systematic adjustment for subjective changes in smoothness of the density estimate.

Mark J. Brewer

[1] S. Weisberg,et al. An Introduction to Regression Graphics , 1994 .

[2] Bu. Park,et al. Rejoinder to ``Practical performance of several data driven bandwidth selectors" , 1992 .

[3] Mark J. Brewer,et al. A comparison of hybrid strategies for Gibbs sampling in mixed graphical models , 1996 .

[4] C. Robert,et al. Estimation of Finite Mixture Distributions Through Bayesian Sampling , 1994 .

[5] B. Silverman. Density estimation for statistics and data analysis , 1986 .

[6] J. Besag,et al. Spatial Statistics and Bayesian Computation , 1993 .

[7] M. C. Jones,et al. A reliable data-based bandwidth selection method for kernel density estimation , 1991 .

[8] M. C. Jones,et al. A Brief Survey of Bandwidth Selection for Density Estimation , 1996 .