Akaike information criterion (AIC) and Bayesian information criterion (BIC) for generalized power Weibull(GPW) distribution

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Description

The function abic.gp.weibull() gives the loglikelihood, AIC and BIC values assuming an generalized power Weibull(GPW) distribution with parameters alpha and theta.

Usage

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abic.gp.weibull(x, alpha.est, theta.est) 

Arguments

x

vector of observations

alpha.est

estimate of the parameter alpha

theta.est

estimate of the parameter theta

Value

The function abic.gp.weibull() 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.gp.weibull for PP plot and qq.gp.weibull for QQ plot

Examples

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## Load data sets
data(repairtimes)
## Maximum Likelihood(ML) Estimates of alpha & theta for the data(repairtimes)
## Estimates of alpha & theta using 'maxLik' package
## alpha.est = 1.566093, theta.est = 0.355321

## Values of AIC, BIC and LogLik for the data(repairtimes)
abic.gp.weibull(repairtimes, 1.566093, 0.355321)

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