gof: Goodness-of-fit test for the efficient closed-form estimators

In MLEce: Asymptotic Efficient Closed-Form Estimators for Multivariate Distributions

Goodness-of-fit test for the efficient closed-form estimators

Description

Goodness-of-fit test for the efficient closed-form estimators

Usage

gof(x, digits = max(3, getOption("digits") - 3), ...)

## S3 method for class 'gof'
print(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

x	an object of class "MLEce" made by the function MLEce.
digits	a numeric number of significant digits.
...	additional arguments affecting the goodness-of-fit test.

Details

Generalized Cramer-von Mises test (chiu and Liu, 2009) is applied to do the goodness-of-fit test for multivariate distributions. For the bivariate gamma and Dirichlet distributions, the L2-symmetric discrepancy (SD2) statistics are applied. But the L2-centred discrepancy (CD2) statistics are applied in the bivariate Weibull distribution.

References

chiu, S. N. and Liu, K. I. (2009) Generalized Cramer-Von Mises goodness-of-fit tests for multivariate distributions. Computational Statistics and Data Analysis, 53, 3817-3834.

Examples

data_BiGam <-  rBiGam(100, c(1,4,5))
res_BiGam  <- MLEce(data_BiGam, "BiGam")
gof(res_BiGam)
datt = rBiWei(n=50, c(4,3,3,4,0.6))
est_BiWei <-MLEce(datt, "BiWei")
gof(est_BiWei)
data_Diri <- LaplacesDemon::rdirichlet(n=60, c(3,1,2,4))
est_Diri <- MLEce(data_Diri, "Dirichlet")
gof(est_Diri)

MLEce documentation built on Sept. 27, 2023, 5:08 p.m.