[R] Minimizing the negative log likelihood function...

From: François Aucoin <frank.aucoin_at_gmail.com>
Date: Tue, 03 Jun 2008 21:54:02 -0300


The following is a function I wrote which generates random variables from a Kappa (2-parameter) distribution.

rkappa <- function(n,beta,alpha){
 if(alpha <= 0)
  stop("alpha must be greater than zero!")  if(beta <= 0)
  stop("beta must be greater than zero!")  Vec <- beta*exp((1/alpha)*(log(-(alpha/(-1 + exp(alpha*log(runif(n,0,1))))))+ alpha*log(runif(n,0,1))))  return(Vec)

Now I would like to estimate the parameters of such a distribution using the Maximum likelihood method.
I know that I have to minimize the following negative log likelihood function:

Neg.Log.Like <- function(beta,alpha,x){
 -(sum( log((alpha/beta)*(alpha + (x/beta)^alpha)^( -(alpha + 1)/alpha))))

I have tried several R's functions for optimization but the results I yield are not correct.
Is there anybody who can help me?


Francois Aucoin

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