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

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Description

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

Usage

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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

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## 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)

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