abic.moee: Akaike information criterion (AIC) and Bayesian information...

In reliaR: Comprehensive Tools for some Probability Distributions

Akaike information criterion (AIC) and Bayesian information criterion (BIC) for the Marshall-Olkin Extended Exponential(MOEE) distribution

Description

The function abic.moee() gives the loglikelihood, AIC and BIC values assuming an MOEE distribution with parameters alpha and lambda.

Usage

abic.moee(x, alpha.est, lambda.est)

Arguments

x	vector of observations
alpha.est	estimate of the parameter alpha
lambda.est	estimate of the parameter lambda

Value

The function abic.moee() gives the loglikelihood, AIC and BIC values.

References

Konishi., S. and Kitagawa, G.(2008). Information Criteria and Statistical Modeling, Springer Science+Business Media, LLC.

See Also

pp.moee for PP plot and qq.moee for QQ plot

Examples

## Load data set
data(stress)
## Estimates of alpha & lambda using 'maxLik' package
## alpha.est = 75.67982, lambda.est = 1.67576
abic.moee(stress, 75.67982, 1.67576)

reliaR documentation built on Nov. 5, 2025, 6:57 p.m.