Test of independence for generalized Farlie-Gumbel-Morgenstern distributions

Date

2008-01-15

Author

Guven, Bilgehan
Kotz, Samual

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Given a pair of absolutely continuous random variables (X, Y) distributed as the generalized Farlie-Gumbel-Morgenstern (GFGM) distribution, we develop a test for testing the hypothesis: X and Y are independent vs. the alternative; X and Y are positively (negatively) quadrant dependent above a preassigned degree of dependence. The proposed test maximizes the minimum power over the alternative hypothesis. Also it possesses a monotone increasing power with respect to the dependence parameter of the GFGM distribution. An asymptotic distribution of the test statistic and an approximate test power are also studied.

Subject Keywords

Approximate test power, Quadrant dependence, Likelihood ratio, Central limit theorem, Independence

URI

https://hdl.handle.net/11511/65621

Journal

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

DOI

https://doi.org/10.1016/j.cam.2006.11.029

Collections

Department of Statistics, Article

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Citation Formats

B. Guven and S. Kotz, “Test of independence for generalized Farlie-Gumbel-Morgenstern distributions,” JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, pp. 102–111, 2008, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/65621.