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

In reliaR: Package for some probability distributions.

Description

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

Usage

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

## 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)

Example output

$LogLik
[1] -734.5823

$AIC
[1] 1473.165

$BIC
[1] 1478.584

reliaR documentation built on May 1, 2019, 9:51 p.m.