Nonparametric Density Estimation for Stratified Samples
In this paper, we consider the non-parametric, kernel estimate of the density, f(x), for data drawn from stratified samples. Much of the data used by social scientists is gathered in some type of complex survey violating the usual assumptions of independently and identically distributed data. Such effects induced by the survey structure are rarely considered in the literature on non-parametric density estimation, yet they may have serious consequences for our analysis, as shown in this paper. A weighted estimator is developed which provides asymptotically unbiased density estimation for stratified samples. A data-based method for choosing the optimal bandwidth is suggested, using information on withinstratum variances and means. The weighted estimator and proposed bandwidth are shown to give smaller mean squared error for stratified samples than an un-weighted estimator and a commonly used method of choosing the bandwidth. Surprisingly, the single bandwidth outperforms optimally choosing stratum-specific bandwidths in some cases. Several illustrations from simulation are provided. We also show that the optimal sampling scheme in this case is always stratified sampling proportional to size, irrespective of the stratum-specific densities
|Date of creation:||Feb 2001|
|Date of revision:||Nov 2005|
|Contact details of provider:|| Postal: Canberra, ACT 2601|
Phone: +61 2 6125 3807
Fax: +61 2 6125 0744
Web page: http://rse.anu.edu.au/
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Pagan,Adrian & Ullah,Aman, 1999.
Cambridge University Press, number 9780521355643, January.
- Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521586115, January.
- Robert Breunig, 2001. "Density Estimation For Clustered Data," Econometric Reviews, Taylor & Francis Journals, vol. 20(3), pages 353-367. Full references (including those not matched with items on IDEAS)
When requesting a correction, please mention this item's handle: RePEc:acb:cbeeco:2005-459. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ()
If references are entirely missing, you can add them using this form.