Akaike information criterion (AIC) and Bayesian information criterion (BIC) for Gumbel distribution

Description

The function abic.gumbel() gives the loglikelihood, AIC and BIC values assuming an Gumbel distribution with parameters mu and sigma.

Usage

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abic.gumbel(x, mu.est, sigma.est)

Arguments

x

vector of observations

mu.est

estimate of the parameter mu

sigma.est

estimate of the parameter sigma

Value

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

References

Akaike, H. (1978). A new look at the Bayes procedure, Biometrika, 65, 53-59.

Claeskens, G. and Hjort, N. L. (2008). Model Selection and Model Averaging, Cambridge University Press, London.

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

Schwarz, S. (1978). Estimating the dimension of the model, Annals of Statistics, 6, 461-464.

Spiegelhalter, D. J., Best, N. G., Carlin, B. P. and van der Linde, A. (2002). Bayesian measures of complexity and fit, Journal of the Royal Statistical Society Series B 64, 1-34.

See Also

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

Examples

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## Load data sets
data(dataset2)
## Maximum Likelihood(ML) Estimates of mu & sigma for the data(dataset2)
## Estimates of mu & sigma using 'maxLik' package
## mu.est = 212.157, sigma.est = 151.768

## Values of AIC, BIC and LogLik for the data(dataset2)
abic.gumbel(dataset2, 212.157, 151.768)

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