Re: st: Imputing for missing proportions

[Geomina Turlea <geomina@yahoo.fr>]

Thank you! 
I did start by making one betafit (or glm, both work ok) estimation of the whole sample and I was intending to simply use the whole predicted values series for further analysis. However, I am not sure if I can provide relevant enough confidence intervals for my missing values to do some further sensitivity analysis...  

_________________________________________Geomina Turlea
TODO AQUEL QUE SUEÑA SE CONVIERTE EN ARTISTA


--- On Fri, 4/12/13, Nick Cox <njcoxstata@gmail.com> wrote:

> From: Nick Cox <njcoxstata@gmail.com>
> Subject: Re: st: Imputing for missing proportions
> To: "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
> Date: Friday, April 12, 2013, 1:49 PM
> Well, imputation of missing values is
> vastly oversold any way. Missing
> at random? I don't (usually) believe it. (Highly unofficial
> opinion.)
> Nick
> njcoxstata@gmail.com
> 
> 
> On 12 April 2013 11:44, Geomina Turlea <geomina@yahoo.fr>
> wrote:
> > I know, but - mi impute- does not support glm either
> >
> > _________________________________________Geomina
> Turlea
> > TODO AQUEL QUE SUEÑA SE CONVIERTE EN ARTISTA
> >
> >
> > --- On Fri, 4/12/13, Nick Cox <njcoxstata@gmail.com>
> wrote:
> >
> >> From: Nick Cox <njcoxstata@gmail.com>
> >> Subject: Re: st: Imputing for missing proportions
> >> To: "statalist@hsphsun2.harvard.edu"
> <statalist@hsphsun2.harvard.edu>
> >> Date: Friday, April 12, 2013, 1:35 PM
> >> I haven't looked at whether it mixes
> >> with -mi-, but -glm- with
> >> -link(logit)- is a standard way to handle
> continuous
> >> proportions.
> >>
> >> Nick
> >> njcoxstata@gmail.com
> >>
> >>
> >> On 12 April 2013 11:08, Geomina Turlea <geomina@yahoo.fr>
> >> wrote:
> >> > Maarten,
> >> > Thank you very much for your answer.
> >> > The problem with -mi impute - is that it does
> not
> >> really have an option for regressing proportions. I
> can't
> >> really use truncated regression, and my dependent
> variable
> >> is not binary or categorial, but a continous
> variable betwen
> >> 0 and 1.
> >> > I am considering to simulate the multiple
> imputation
> >> with a beta regression for estimation of the
> missing
> >> values.
> >> > Very gratefull for an yes/no opinion on this,
> >> > Geomina
> >> >
> >> >
> >> > --- On Thu, 4/11/13, Maarten Buis <maartenlbuis@gmail.com>
> >> wrote:
> >> >
> >> >> From: Maarten Buis <maartenlbuis@gmail.com>
> >>
> >> Geomina Turlea wrote:
> >>
> >> >> > I am fighting for a while with
> estimate
> >> missing data
> >> >> for the share of ICT professionals/total
> >> employment, in 59
> >> >> industries, 27 EU countries and for 14
> years.
> >> >> > This data exists in the European
> Labour Force
> >> Survey,
> >> >> but the dataset is incomplete.
> >> >> >
> >> >> > 1. Can I use mi impute with
> proportions?
> >> >> > 2. I used betafit to fit a
> distribution with
> >> values
> >> >> between 0 and 1. Than I imputed the
> missing values
> >> from the
> >> >> estimated beta distribution. Is this
> method
> >> >> superior/inferior to using mi impute?
> >> >> > 3. I tried to use the
> Kolmogorov-Smirnov test,
> >> but I
> >> >> don't know what I got wrong. Below is a
> sequence
> >> where I
> >> >> created a variable with the distribution
> beta and
> >> then test
> >> >> the hypothesis with the K-S test. The test
> rejects
> >> the null
> >> >> hypothesis that the data has the
> distribution I
> >> used to
> >> >> create it. How could that be?
> >> >> >
> >> >> > . gen x=rbeta(0.05, 1.77)
> >> >> > . ksmirnov x=rbeta(0.05, 1.77)
> >>
> >> >> My first step would be to look at the
> industries
> >> with
> >> >> missing values.
> >> >> Sometimes missing just means 0 or
> negligable, and
> >> looking at
> >> >> the
> >> >> industries would give you a fair guess of
> whether
> >> that is
> >> >> the case. If
> >> >> that is the case your imputation problem
> reduces to
> >> just a
> >> >> recoding
> >> >> problem.
> >> >>
> >> >> For questions 2 and 3: If you have an
> imputation
> >> problem,
> >> >> then you
> >> >> should use -mi- and not -betafit-
> (available from
> >> SSC),
> >> >> because that
> >> >> is what -mi- was designed for.
> >> >>
> >> >> For question 3: -rbeta()- gives you random
> numbers
> >> from a
> >> >> beta
> >> >> distribution, so that is definately not
> something
> >> you want
> >> >> to feed in
> >> >> -ksmirnov-. I just would use either
> -margdistfit-
> >> or
> >> >> -hangroot- (also
> >> >> available from SSC) after -betafit- to
> check the
> >> fit.
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