Hi everyone,
I have a problem with maximum-likelihood-estimation in the following
situation:
Assume a functional relation y = f(x) (the specific form of f should be
irrelevant). For my observations I assume (for simplicity) white noise,
such that hat(y_i) = f(x_i) + epsilon_i, with the epsilon_i
Hi all,
I am trying to find out how a certain functionality is implemented in R
respectively what a certain found does exactly.
Specifically I am interested in multivariate kernel density estimation.
I found the "ks" package and its "kde" function. Usually, my preferred
way to "look under the hoo
Thank you David and Thierry, your answers helped a lot!
Kind regards,
RK.
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Dear all,
I have a problem when trying to present the results of several
regression. Say I have run several regressions on a dataset and saved
the different results (as in the mini example below). I then want to
loop over the regression results in ord
k out the details.
Thanks a lot anyways for solving my coding problem!
RK
On 22.07.2014 15:26, peter dalgaard wrote:
>
> On 22 Jul 2014, at 06:04 , David Winsemius
> wrote:
>
>>
>> On Jul 21, 2014, at 12:10 PM, Ronald Kölpin wrote:
>>
>>> Dear R-Co
ps[,1])
gaps[,4] <- log(gaps[,2])
nll <- function(mu, sigma)
{
if(sigma >= 0 && mu >= 0)
{
-sum(log(pnorm(gaps[,3], mean=mu, sd=sigma) - pnorm(gaps[,4],
mean=mu, sd=sigma)))
}
else
{
NA
}
}
fit <- mle(nll, start=list(mu=0, sigma=1), nobs=10)
pri
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