cc.mle: Maximum likelihood estimation of the CC distribution

In CCd: The Cauchy-Cacoullos (Discrete Cauchy) Distribution

Maximum likelihood estimation of the CC distribution

Description

Maximum likelihood estimation of the CC distribution.

Usage

cc.mle(y)
cc.mle0(y, tol = 1e-7)

Arguments

y	A vector with integer values.
tol	The tolerance value to terminate the maximization algorithm.

Details

The function cc.mle0() uses the optimize function to perform MLE when the location parameter is zero, just as proposed by Papadatos (2022). The function cc.mle() uses the optim function when the location is not assumed zero.

Value

A list including:

param	For the cc.mle() a vector of the \lambda and \mu parameters.
lambda	For the cc.mle0() the \lambda parameter.
loglik	The value of the maximized log-likelihood.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail].

References

Papadatos N. (2022). The characteristic function of the discrete Cauchy distribution In Memory of T. Cacoullos. Journal of Statistical Theory and Practice, 16(3): 47.

See Also

loc0.test, dcc, cc.reg

Examples

y <- round( rcauchy(100, 3, 10) )
cc.mle(y)

y <- round( rcauchy(100, 0, 10) )
cc.mle0(y)

CCd documentation built on April 4, 2025, 2:21 a.m.