[ADMB Users] mildly robust methods in count models

[H. Skaug]

I think it is a good idea check if robustness changes the conclusion
of published
empirical studies. That would be an interesting study in itself.

About your suggestion of using glmmADMB
I think that should be easily done an a case by case basis.
What requires a lot of work however, is to "robustify glmmADMB"
as an R-package. I imagine that would involve changing all the postprocessing
stuff that is written in R (diagnostics, etc).

Hans

On Fri, Dec 14, 2012 at 6:13 PM, dave fournier <davef at otter-rsch.com> wrote:
> On 12-12-14 09:04 AM, H. Skaug wrote:
>
> I think that there are a number of issues. Two that come to mind are
>
> First is to verify my claim for each type of example
> that these methods perform nearly as well as the nonrobust methods when the
> null hypothesis is satisfied.  This would  show that there is (virtually) no
> risk in using them.
>
> Another issue is to investigate real examples to see how often one gets
> different
> results for various estimates and tests.  It would be nice to use the
> glmmadmb framework
> if possible.  So long as glmmadmb can create the correct dat and pin files
> it is simple
> to modify the glmmadmb.tpl to do the robust estimates.  This should enable
> us to
> use a lot of data sets that are available in R.  I quess that R users would
> also find the results
> more accessible conceptually.
>
>
>
>> Hi,
>>
>> I agree fully with you about the need for robust methods and that ADMB
>> is a great
>> tool for implementing such. However, and "robustification" is a generic
>> tool and should be documented separately from other modelling issues.
>> I think we need a document (with small  tpl examples), written in a
>> pedagogical way,
>> describing  how all the standard probability distributions should
>> be robustified. That would be a good resource for people who want to
>> learn about robust methods.
>>
>> If somebody is willing to develop such a suite I will upload it
>> immediately as
>> an example on the webpage, but currently I am not able to develop this
>> from scratch  myself.
>>
>> Hans
>>
>> On Fri, Dec 14, 2012 at 5:09 PM, dave fournier <davef at otter-rsch.com>
>> wrote:
>>>
>>> On 12-12-13 08:43 PM, Hans J. Skaug wrote:
>>>
>>> Hi,
>>>
>>> BTW Hans, I think you misunderstood my remarks about robustness in
>>> count models. I haven't got any response so I am kicking the wasp nest
>>> again.
>>>   My feeling is that there appear to be two schools,
>>> those who blindly use standard non-robust methods and those
>>> who use very robust methods.  While there may be situations
>>> where very robust methods are a good idea, what I am advocating
>>> is to routinely use mildly robust methods.  My reasoning is that
>>> mildly robust methods perform almost as well as the standard
>>> methods when the non robust hypothesis is satisfied and
>>> they perform much better when just a small amount of
>>> contamination is introduced.   I don't think Ben gets this point.
>>> He notes that the point estimates are nearly the same.  This
>>> is just like the fact that for normal theory estimates of means
>>> are more insensitive to outliers than estimates of variances.
>>> However it is the estimates of variances that are important
>>> for significance tests.
>>>
>>> To test out these ideas I took Mollies negative binomial model with the
>>> fir
>>> data and
>>> added a covariate which was random noise.  With the non-robust
>>> model including this covariate  was considered significant 7.7% of
>>> the time while the robust version it was 5.4% of the time, much closer
>>> to the theoretical value of 5%.  Why do I think this is an ADMB thing?
>>>
>>> The reason that the R gurus avoid these ideas is that they don't
>>> fit into their simple exponential family methodology.  With ADMB it is
>>> is a trivial extension to the model.
>>>
>>> A major problem (and I know I have said this before) with promoting ADMB
>>> is
>>> that it always seems that to compare it so say R you have to use methods
>>> that
>>> R deems to be legitimate.  So these mildly robust methods never come up.
>>> Rather the question is always posed as either standard non-robust methods
>>> or
>>> extreme robust methods like Huber stuff.  I think that ADMB can do a
>>> great
>>> service to
>>> the statistical community by making these mildly robust methods more
>>> widely known.
>>>
>>> For the record the mildly robust method for the negative binomial with
>>> parameters mu and tau
>>> where tau>1 is the overdispersion would be to use something like
>>>
>>>                    (1-p)*NB(mu,tau)+p*NB(mu,q*tau)
>>>
>>> where p and q may be estimated or held fixed.  Typical values for p and q
>>> are .05 and 9.0.
>>> For mollies fir model these were estimated.
>>>
>>> This all came up first when I came across an interesting article on the
>>> web
>>> which
>>> stated (and seemed to demonstrate)
>>> that max like should never be used in NB count models for significance
>>> tests
>>> on the
>>> fixed effects because this lack of robustness led  to rejecting the null
>>> hypothesis
>>> too often.  They advocated quasi-likelihood or GEE instead.   As a fan of
>>> max like
>>> I wondered if it could be "fixed" for these models.
>>>
>>>   Wish I could find that article again.
>>>
>>>             Dave
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> _______________________________________________
>>> Users mailing list
>>> Users at admb-project.org
>>> http://lists.admb-project.org/mailman/listinfo/users
>
>