reliaR (version 0.01)

abic.logis.exp: Akaike information criterion (AIC) and Bayesian information criterion (BIC) for Logistic-Exponential(LE) distribution

Description

The function abic.logis.exp() gives the loglikelihood, AIC and BIC values assuming an Logistic-Exponential(LE) distribution with parameters alpha and lambda.

Usage

abic.logis.exp(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.logis.exp() 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.logis.exp for PP plot and qq.logis.exp for QQ plot

Examples

## Load data sets
data(bearings)
## Maximum Likelihood(ML) Estimates of alpha & lambda for the data(bearings)
## Estimates of alpha & lambda using 'maxLik' package
## alpha.est = 2.36754, lambda.est = 0.01059

## Values of AIC, BIC and LogLik for the data(bearings)
abic.logis.exp(bearings, 2.36754, 0.01059)