Smooth Test For Testing Equality Of Two Densities
It has been a conventional wisdom that the two-sample version of the goodness-of-fit test like the Kolmogorov-Smirnov, CramÃ©r-von Mises and Anderson-Darling tests fail to have good power particularly against very specific alternatives. We show that a modified version of Neyman Smooth test that can also be derived as a score test based on the empirical distribution functions obtained from the two samples remarkably improves the detection of directions of departure. We can identify deviations in different moments like the mean, variance, skewness or kurtosis terms using the Ratio Density Function. We derive a bound on the relative sample sizes of the two samples for a consistent test and an "optimal" choice range of the sample sizes to ensure minimal size distortion in finite samples. We apply our procedure to compare the age distributions of employees insured with small employers
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
|Date of creation:||11 Aug 2004|
|Date of revision:|
|Contact details of provider:|| Phone: 1 212 998 3820|
Fax: 1 212 995 4487
Web page: http://www.econometricsociety.org/pastmeetings.aspEmail:
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:ecm:feam04:714. 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: (Christopher F. Baum)
If references are entirely missing, you can add them using this form.