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maximum likelihood sstimation of beta-normal in R

I want to estimate the parameters of beta-normal distribution.I've used the maxLik package in r:

library(VGAM)

library(maxLik)

alfa=2;beta=3;mu=0;sigma=1

n=100

x=rbetanorm(n,alfa,beta,mu,sigma)

logLikFun=function(w){

alfa=w[1]

beta=w[2]

mu=w[3]

sigma=w[4]

ll={-n*log(beta(alfa,beta))+(alfa-1)*sum(log(pnorm((x-mu)/sigma,mean=0,sd=1)))+(beta-1)*sum(log(1-pnorm((x-mu)/sigma,mean=0,sd=1)))-n*log(sigma)+sum(log(dnorm((x-mu)/sigma,mean=0,sd=1)))}

ll

}

mle=maxLik(logLikFun,start=c(alfa=3,beta=2,mu=1,sigma=2))

summary(mle)

i want to estimate the parameters of beta-normal distribution.I've used the maxLik package

library(VGAM)

library(maxLik)

alfa=2;beta=3;mu=0;sigma=1

n=100

x=rbetanorm(n,alfa,beta,mu,sigma)

logLikFun=function(w){

alfa=w[1]

beta=w[2]

mu=w[3]

sigma=w[4]

ll={-n*log(beta(alfa,beta))+(alfa-1)*sum(log(pnorm((x-mu)/sigma,mean=0,sd=1)))+(beta-1)*sum(log(1-pnorm((x-mu)/sigma,mean=0,sd=1)))-n*log(sigma)+sum(log(dnorm((x-mu)/sigma,mean=0,sd=1)))}

ll

}

mle=maxLik(logLikFun,start=c(alfa=3,beta=2,mu=1,sigma=2))

summary(mle)

but it gives error:

----------------------------------

Maximum Likelihood estimation

Newton-Raphson maximisation, 4 iterations

Return code 2: successive function values within tolerance limit

Log-Likelihood: -86.16515

4 free parameters

Estimates:

Estimate Std. error t value Pr(> t)

alfa 3.000 Inf 0 1

beta 2.941 Inf 0 1

mu 1.000 Inf 0 1

sigma 2.000 Inf 0 1

--------------------------------------------

the problem is the infinite value for the errors which is not acceptable. I would be pleased if someone could solve this problem.

Færdigheder: Matlab and Mathematica, Programmeringssproget R, Software Udvikling, Statistisk analyse, Statistikker

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