Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1151032 | Statistical Methodology | 2009 | 12 Pages |
Abstract
Goodness-of-fit tests for the family of symmetric normal inverse Gaussian distributions are constructed. The tests are based on a weighted integral incorporating the empirical characteristic function of suitably standardized data. An EM-type algorithm is employed for the estimation of the parameters involved in the test statistic. Monte Carlo results show that the new procedure is competitive with classical goodness-of-fit methods. An application with financial data is also included.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
K. Fragiadakis, D. Karlis, S.G. Meintanis,