Calculate Akaike Information Criterion (AIC) for Generalized Pareto Distribution

This function estimates the shape1, shape2, and rate parameters of a generalized Pareto distribution from the provided data using maximum likelihood estimation, and then calculates the AIC value based on the fitted distribution.

Usage

util_generalized_pareto_aic(.x)

Arguments

.x: A numeric vector containing the data to be fitted to a generalized Pareto distribution.

Value

The AIC value calculated based on the fitted generalized Pareto distribution to the provided data.

Details

This function calculates the Akaike Information Criterion (AIC) for a generalized Pareto distribution fitted to the provided data.

This function fits a generalized Pareto distribution to the provided data using maximum likelihood estimation. It estimates the shape1, shape2, and rate parameters of the generalized Pareto distribution using maximum likelihood estimation. Then, it calculates the AIC value based on the fitted distribution.

Initial parameter estimates: The function uses the method of moments estimates as starting points for the shape1, shape2, and rate parameters of the generalized Pareto distribution.

Optimization method: The function uses the optim function for optimization. You might explore different optimization methods within optim for potentially better performance.

Goodness-of-fit: While AIC is a useful metric for model comparison, it's recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.

Other Utility: check_duplicate_rows(), convert_to_ts(), quantile_normalize(), tidy_mcmc_sampling(), util_beta_aic(), util_binomial_aic(), util_cauchy_aic(), util_chisq_aic(), util_exponential_aic(), util_f_aic(), util_gamma_aic(), util_generalized_beta_aic(), util_geometric_aic(), util_hypergeometric_aic(), util_inverse_burr_aic(), util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), util_negative_binomial_aic(), util_normal_aic(), util_paralogistic_aic(), util_pareto1_aic(), util_pareto_aic(), util_poisson_aic(), util_t_aic(), util_triangular_aic(), util_uniform_aic(), util_weibull_aic(), util_zero_truncated_binomial_aic(), util_zero_truncated_geometric_aic(), util_zero_truncated_negative_binomial_aic(), util_zero_truncated_poisson_aic()

Author

Steven P. Sanderson II, MPH

Examples

# Example 1: Calculate AIC for a sample dataset
set.seed(123)
x <- actuar::rgenpareto(100, shape1 = 1, shape2 = 2, scale = 3)
util_generalized_pareto_aic(x)
#> [1] 742.2865