gpdImAsym: GPD Asymptotic Adjusted Information Matrix (IM) Test

In eva: Extreme Value Analysis with Goodness-of-Fit Testing

Description

Runs the IM Test using bootstrap estimated covariance matrix. Asymptotically (in sample size) follows the F(3, bootnum - 3) distribution (see reference for details).

Usage

gpdImAsym(data, bootnum, theta = NULL)

Arguments

data	Data should be in vector form.
bootnum	Number of bootstrap replicates for the covariance estimate.
theta	Estimate for theta in the vector form (scale, shape). If NULL, uses the MLE.

Value

statistic	Test statistic.
p.value	P-value for the test.
theta	Value of theta used in the test.
effective_bootnum	Effective number of bootstrap replicates used for the covariance estimate. If a replicate fails to converge, it will not be used in the estimation.

References

Dhaene, G., & Hoorelbeke, D. (2004). The information matrix test with bootstrap-based covariance matrix estimation. Economics Letters, 82(3), 341-347.

Examples

# Generate some data from GPD
x <- rgpd(200, loc = 0, scale = 1, shape = 0.2)
gpdImAsym(x, bootnum = 50)

eva documentation built on Jan. 13, 2021, 8:34 p.m.