abic.chen: Akaike information criterion (AIC) and Bayesian information criterion (BIC) for a sample from Chen distribution

Description

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

Usage

abic.chen(x, beta.est, lambda.est)

Value

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

Arguments

x: vector of observations
beta.est: estimate of the parameter beta
lambda.est: estimate of the parameter lambda

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.

Examples

Run this code

## Load data sets

data(sys2)
## Maximum Likelihood(ML) Estimates of beta & lambda for the data(sys2)
## beta.est = 0.262282404, lambda.est = 0.007282371

## Values of AIC, BIC and LogLik for the data(sys2) 
abic.chen(sys2, 0.262282404, 0.007282371)