On Fri, 25 Feb 2000, Steve Cumming wrote:
>
>
> I am using Splus 3.4 under SGI IRIX 5.3.
>
> My problem is poisson regression, where my data y_i
> are counts assumed to be distributed as a Poisson
> r.v with mean
>
> \sum_{j=1}^{n} \lambda_j p_i,j
>
> where p_i are vectors of independent variables which sum to 1,
> and \lambda is vector of parameters to be estimated.
>
> With simulated data, I find using nlminb to directly minimise the
> log-likelihood produces unbiased parameter estimates, compared
> to the results of glm(family=poisson).
>
> My problem is in determining the standard errors of the parameter
> estimates, which I need for prediction.
>
> To be precise, here are several ways of obtaining the variables p_i,
> and one of the parameters is crucial for the intended application.
> Thus, my criteria for the best model is that which minimises the
> prediction error associated with that one parameter. Hence, the
> need for standard errors.
>
> Is there some way of computing these directly from the value returned
> by nlminb, or must I resort to bootstapping?
See vcov.nlminb in library MASS.
--
Brian D. Ripley, ripley@stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272860 (secr)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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