You will have to modify your likelihood in such a way that it also
includes the weights. If your likelihood has the following form: l =
sum(log p_i) you could for example add the weights to the likelihood:
lw = sum(w_i * log p_i) (although I am not sure that this is the
correct way to add the weights in your case)
Therefore, you will have to change your functions negllh and se to
also accepts the weights as input parameters:
negllh <- function([PARAMETERS YOU HAVE ALREADY HERE], weights, ...) }{ ... }
se <- function([PARAMETERS YOU HAVE ALREADY HERE], weights, ...) { ... }
You can then call optim adding weights as additional parameter which
gets passed on to negllh and se:
gh.fit = try(optim(vega, negllh,hessian = se,pdf = gh.pdf,
tmp.data = data, transf = transform, const.pars = vars[!opt.pars],
silent = silent, par.names = names(vars), weights = w, ...))
HTH,
Jan
Quoting JASON SCALLY <j.scally at student.qut.edu.au>:
Hi All,
I am trying to code an R script which gives me the time varying
parameters of the NIG and GH distributions. Further, becasue I think
these these time varying parameters should be more responsive to
more recent observations, I would like to include a weighted
likelihood estimation proceedure where the observations have an
exponentially decaying weighting rather than the equal weighting
implied by a standard MLE approach.
The optimization function which leads to my parameter estimates is given by;
gh.fit = try(optim(vega, negllh,hessian = se,pdf = gh.pdf,
tmp.data = data, transf = transform, const.pars = vars[!opt.pars],
silent = silent, par.names = names(vars), ...))
param.est = gh.fit$par
In the above, vega are the parameters to estimate and pdf is the GH
pdf. This seems to work well for the case where observations are
equally weighted. However, I'm stuck on how to include a weighted
vector (w_i) to turn this problem into a weighted ML optimization.
Would you please be able to suggest a function or change in code
which may allow me to do this?
Thank you in advance for your time.
Jason
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