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Re: st: RE: Reconstructing the distribution from interval data
From
Austin Nichols <[email protected]>
To
[email protected]
Subject
Re: st: RE: Reconstructing the distribution from interval data
Date
Wed, 8 Sep 2010 14:34:24 -0400
David Radwin <[email protected]> & Ronan Conroy:
The ML approach seems preferable; for income data, see e.g.
http://fmwww.bc.edu/repec/bocode/g/gbgfit.html
and if you assume a family of distributions for #procedures
(Poisson, maybe?), you can estimate those parameters the same way.
On Wed, Sep 8, 2010 at 1:26 PM, David Radwin <[email protected]> wrote:
> You might check this thread:
>
> http://www.stata.com/statalist/archive/2007-09/msg00597.html
>
> although the examples (income and patient visits) are not exactly similar.
>
> David
> --
> David Radwin
> Research Associate
> MPR Associates, Inc.
> 2150 Shattuck Ave., Suite 800
> Berkeley, CA 94704
> Phone: 510-849-4942
> Fax: 510-849-0794
>
> www.mprinc.com
>
>
>> -----Original Message-----
>> From: [email protected] [mailto:owner-
>> [email protected]] On Behalf Of Ronan Conroy
>> Sent: Wednesday, September 08, 2010 9:25 AM
>> To: statalist hsphsun2. harvard. edu
>> Subject: st: Reconstructing the distribution from interval data
>>
>> I'm reviewing a paper in which doctors were asked how many times they
>> had carried out a particular procedure in the last year. The
>> questionnaire used categories, so responses looked like this:
>>
>> Number of Gynaecologists Plastic
>> times Surgeons
>>
>> None 17 3
>> 1-5 24 34
>> 5-10 0 4
>> 10-20 0 1
>> More than 20 0 1
>>
>> I am wondering how one might calculate the likeliest underlying annual
>> rate for each group based on the data above.
>>
>> Has anyone come up against this problem and found a solution? I'd be
>> very curious about the answer for practical reasons - the authors have
>> data that are pretty hard to come by, and being able to calculate an
>> annual rate would be very helpful indeed for future research.
>>
>>
>>
>>
>> Ronán Conroy
>> Associate Professor
>> Division of Population Health Sciences
>> =================================
>
>
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