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From |
Geomina Turlea <geomina@yahoo.fr> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Imputing for missing proportions |

Date |
Fri, 12 Apr 2013 11:56:45 +0100 (BST) |

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. > >> * > >> * For searches and help try: > >> * http://www.stata.com/help.cgi?search > >> * http://www.stata.com/support/faqs/resources/statalist-faq/ > >> * http://www.ats.ucla.edu/stat/stata/ > >> > > > > * > > * For searches and help try: > > * http://www.stata.com/help.cgi?search > > * http://www.stata.com/support/faqs/resources/statalist-faq/ > > * http://www.ats.ucla.edu/stat/stata/ > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/statalist-faq/ > * http://www.ats.ucla.edu/stat/stata/ > * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**Re: st: Imputing for missing proportions***From:*Nick Cox <njcoxstata@gmail.com>

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