abic.gp.weibull: Akaike information criterion (AIC) and Bayesian information criterion (BIC) for generalized power Weibull(GPW) distribution

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)

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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