abic.gen.exp

The function <code>abic.gen.exp()</code> gives the <code>loglikelihood</code>, <code>AIC</code> and <code>BIC</code> values
 assuming an Generalized Exponential distribution with parameters alpha and 
 lambda. The function is based on the invariance property of the MLE.

models

Provides a comprehensive suite of utilities for univariate continuous probability distributions and reliability models. Includes functions to compute the probability density, cumulative distribution, quantile, reliability, and hazard functions, along with random variate generation. Also offers diagnostic and model assessment tools  such as Quantile-Quantile (Q-Q) and Probability-Probability (P-P) plots, the Kolmogorov-Smirnov goodness-of-fit test, and model selection criteria including the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Currently implements the following distributions: Burr X, Chen, Exponential Extension, Exponentiated Logistic, Exponentiated Weibull, Exponential Power, Flexible Weibull, Generalized Exponential, Gompertz, Generalized Power Weibull, Gumbel, Inverse Generalized Exponential, Linear Failure Rate, Log-Gamma, Logistic-Exponential, Logistic-Rayleigh, Log-log, Marshall-Olkin Extended Exponential, Marshall-Olkin Extended Weibull, and Weibull Extension distributions. Serves as a valuable resource for teaching and research in probability theory, reliability analysis, and applied statistical modeling.

Vijay Kumar

reliaR

Comprehensive Tools for some Probability Distributions

Uwe Ligges

abic.gen.exp function

<dl> <dt>x</dt>
<dd>vector of observations</dd> <dt>alpha.est</dt>
<dd>estimate of the parameter alpha</dd> <dt>lambda.est</dt>
<dd>estimate of the parameter lambda</dd></dl>

Arguments

Akaike information criterion (AIC) and Bayesian information
 criterion (BIC) for a sample from Generalized Exponential distribution — abic.gen.exp

<dl>

 <dt>x</dt>
<dd>vector of observations</dd>

 <dt>alpha.est</dt>
<dd>estimate of the parameter alpha</dd>

 <dt>lambda.est</dt>
<dd>estimate of the parameter lambda</dd>

</dl>

abic.gen.exp: Akaike information criterion (AIC) and Bayesian information criterion (BIC) for a sample from Generalized Exponential distribution

Description

Usage

Value

Arguments

References

See Also

Examples