BGE: Bivariate Gamma Distribution Estimation

In senhu/mvClaim: Multivariate general insurance claims modelling

Description

This function allows you to estimate bivariate gamma distribution given a data set using EM algorithm

Usage

BGE(data, maxit = 300, tol = 1e-05, start = NULL, verbose = FALSE)

Arguments

data	A numeric vector, matrix or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables.
maxit	An integer limits on the number of EM iterations. The default is 300.
tol	A value giving relative convergence tolerance for the log-likelihood. The default is 1e-6.
start	Starting value of the EM algorithm
verbose	logical; controls whether summary result of each EM iteration is displayed during the fitting procedure. Default is TRUE.

Value

An object of class BGR providing the estimation results. The details of the output components are:

estimate	The estimated parametr values.
loglike	The final estimated maximum log-likelihood value.
ll	The sequence of log-likelihood values in the EM algorithm fitting process.
df	The number of estimated parameters.
AIC	AIC values.
BIC	BIC values.
iter	Total iteration numbers.
n	The number of observations in the data.
call	The matched call.

Examples

dat <- rbivgamma(1000, alpha = c(1,2,0.5), beta=0.1)
mod <- BGE(data = dat, verbose = FALSE)
mod

senhu/mvClaim documentation built on Jan. 29, 2022, 3:18 p.m.