abic.burrX
(Package: reliaR) :
Akaike information criterion (AIC) and Bayesian information criterion (BIC)
The function abic.burrX() gives the loglikelihood, AIC and BIC values assuming an BurrX distribution with parameters alpha and lambda.
● Data Source:
CranContrib
● Keywords: models
● Alias: abic.burrX
●
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abic.chen
(Package: reliaR) :
Akaike information criterion (AIC) and Bayesian information
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.
● Data Source:
CranContrib
● Keywords: models
● Alias: abic.chen
●
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abic.exp.ext
(Package: reliaR) :
Akaike information criterion (AIC) and Bayesian information criterion (BIC)
The function abic.exp.ext() gives the loglikelihood, AIC and BIC values assuming an Exponential Extension(EE) distribution with parameters alpha and lambda.
● Data Source:
CranContrib
● Keywords: models
● Alias: abic.exp.ext
●
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abic.exp.power
(Package: reliaR) :
Akaike information criterion (AIC) and Bayesian information
The function abic.exp.power() gives the loglikelihood, AIC and BIC values assuming Chen distribution with parameters alpha and lambda. The function is based on the invariance property of the MLE.
● Data Source:
CranContrib
● Keywords: models
● Alias: abic.exp.power
●
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abic.expo.logistic
(Package: reliaR) :
Akaike information criterion (AIC) and Bayesian information criterion (BIC)
The function abic.expo.logistic() gives the loglikelihood, AIC and BIC values assuming an Exponentiated Logistic(EL) distribution with parameters alpha and beta.
● Data Source:
CranContrib
● Keywords: models
● Alias: abic.expo.logistic
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abic.expo.weibull
(Package: reliaR) :
Akaike information criterion (AIC) and Bayesian information criterion (BIC)
The function abic.expo.weibull() gives the loglikelihood, AIC and BIC values assuming an Exponentiated Weibull(EW) distribution with parameters alpha and theta.
● Data Source:
CranContrib
● Keywords: models
● Alias: abic.expo.weibull
●
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abic.flex.weibull
(Package: reliaR) :
Akaike information criterion (AIC) and Bayesian information criterion (BIC)
The function abic.flex.weibull() gives the loglikelihood, AIC and BIC values assuming an flexible Weibull(FW) distribution with parameters alpha and beta.
● Data Source:
CranContrib
● Keywords: models
● Alias: abic.flex.weibull
●
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abic.gen.exp
(Package: reliaR) :
Akaike information criterion (AIC) and Bayesian information
The function abic.gen.exp() gives the loglikelihood, AIC and BIC values assuming an Generalized Exponential distribution with parameters alpha and lambda. The function is based on the invariance property of the MLE.
● Data Source:
CranContrib
● Keywords: models
● Alias: abic.gen.exp
●
0 images
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abic.gompertz
(Package: reliaR) :
Akaike information criterion (AIC) and Bayesian information criterion (BIC)
The function abic.gompertz() gives the loglikelihood, AIC and BIC values assuming an Gompertz distribution with parameters alpha and theta.
● Data Source:
CranContrib
● Keywords: models
● Alias: abic.gompertz
●
0 images
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abic.gp.weibull
(Package: reliaR) :
Akaike information criterion (AIC) and Bayesian information criterion (BIC)
The function abic.gp.weibull() gives the loglikelihood, AIC and BIC values assuming an generalized power Weibull(GPW) distribution with parameters alpha and theta.
● Data Source:
CranContrib
● Keywords: models
● Alias: abic.gp.weibull
●
0 images
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