Akaike information criterion (AIC) and Bayesian information criterion (BIC) for Logistic-Rayleigh(LR) distribution

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Description

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

Usage

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

Examples

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## Load data sets
data(stress)
## Maximum Likelihood(ML) Estimates of alpha & lambda for the data(stress)
## Estimates of alpha & lambda using 'maxLik' package
## alpha.est = 1.4779388, lambda.est = 0.2141343

## Values of AIC, BIC and LogLik for the data(stress)
abic.logis.rayleigh(stress, 1.4779388, 0.2141343)

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