reliaR (version 0.01)

abic.expo.logistic: Akaike information criterion (AIC) and Bayesian information criterion (BIC) for Exponentiated Logistic(EL) distribution

Description

The function abic.expo.logistic() gives the loglikelihood, AIC and BIC values assuming an Exponentiated Logistic(EL) distribution with parameters alpha and beta.

Usage

abic.expo.logistic(x, alpha.est, beta.est)

Arguments

x

vector of observations

alpha.est

estimate of the parameter alpha

beta.est

estimate of the parameter beta

Value

The function abic.expo.logistic() 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.expo.logistic for PP plot and qq.expo.logistic for QQ plot

Examples

## Load data sets
data(dataset2)
## Maximum Likelihood(ML) Estimates of alpha & beta for the data(dataset2)
## Estimates of alpha & beta using 'maxLik' package
## alpha.est = 5.31302, beta.est = 139.04515

## Values of AIC, BIC and LogLik for the data(dataset2)
abic.expo.logistic(dataset2, 5.31302, 139.04515)