Akaike information criterion (AIC) and Bayesian information criterion (BIC) for a sample from Exponential Power(EP) distribution

Share:

Description

The function abic.exp.power() gives the loglikelihood, AIC and BIC values assuming Chen distribution with parameters alpha and lambda. The function is based on the invariance property of the MLE.

Usage

1
abic.exp.power(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.exp.power() 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.exp.power for PP plot and qq.exp.power for QQ plot

Examples

1
2
3
4
5
6
7
8
## Load data sets
data(sys2)
## Maximum Likelihood(ML) Estimates of alpha & lambda for the data(sys2)
## alpha.est = 0.905868898, lambda.est =  0.001531423

## Values of AIC, BIC and LogLik for the data(sys2) 

abic.exp.power(sys2, 0.905868898, 0.001531423)

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.