Re: st: Fw: influential observations

[Nick Cox <njcoxstata@gmail.com>]

I don't think it is a good idea to expect a firm statistical answer
based on this information.

1. Isn't there science that will throw light on this question for you?
For example, in my field, the Amazon is often an influential
observationr, as are other very big rivers. But throwing them out just
because they might make modelling awkward would usually be very
strange science. They deserve their votes. Your field, whatever it is,
wil presumably have its own arguments and issues.

2. When there are influential observations in a -glm-, considering a
different link, e.g. reciprocal, is often a good way forward.

3. There are many situations in which one predictor that is
insignificant at conventional levels deserves its place in a model if
it has a logical role.

4. I don't see why you expect normally distributed residuals when the
family is gamma!!! I think that overall plots of residual vs fitted,
observed vs fitted, variance of residual vs fitted, etc., are worth
more attention than the marginal distribution.

Nick

On Tue, Apr 12, 2011 at 6:17 AM, Arti Pandey <rtpandey@yahoo.com> wrote:
> Hello
> A belated thank you to Maarten Buis and David Greenberg for suggestions to my
> previous query.
> I decided to go with -glm- for my model and have been trying to understand the
> different procedures for checking the model
> The anscombe residuals and deviance are  normally distributed, but there are
> three influential observations based upon cooksd.
> On removing these observations, the BIC rises by 10, and one of the predictors
> also becomes insignificant.
> Is the model fitting because of these influential observations now and therefore
>
> not correct?
> I have continuous response data and used gamma distribution with log link.
> Any recommendations for information on model checking after glm are also
> appreciated, the book "glm
>
> and extensions" by Hardin and Hilbe is out of my reach, unless an electronic
> copy is available.

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