Davide wrote:
"I just don't want to create new observations by
filling up the dataset with -invnorm(uniform())- because I want to
preserve the properties of the variables: some are binary.."
Dear Davide, if only variables properties withold you from creating new
observations, you can use -invibeta- for creating random observation with
given parameters a and b (in Stata 9.2/SE it goes like this: g
A=invibeta(a,b,uniform()) for binary variables.
The same approach can be replicated for continuous or counts variables, with
-invgammap-(in Stata 9.2/SE it goes like this: g B=b*invgammap(a,uniform()).
Two interesting textbooks on fitting beta and gamma distributions (as well
as taking advantage from their inverse distributions) are:
Spiegelhalter DJ, Abrams KR, Myles JP. Bayesian approach to clinical trials
and health-care evaluation. Chichester: Wiley, 2004
Gelman A, Carlin JB. Bayesian Data Analysis. Second edition. Boca Raton:
Chapman & Hall/CRC, 2004
Kind Regards,
Carlo
-----Messaggio originale-----
Da: [email protected]
[mailto:[email protected]] Per conto di Davide Cantoni
Inviato: lunedì 22 giugno 2009 14.59
A: [email protected]
Oggetto: Re: st: Bootstrapping new observations to add to an existing
dataset
Thank you, gentlemen. To clarify: yes, In a sense I want more of the
same observations. I just don't want to create new observations by
filling up the dataset with -invnorm(uniform())- because I want to
preserve the properties of the variables: some are binary, some are
integers, and some are just any kind of number. The values of these
variables are irrelevant for my regressions, their underlying
distribution is not: I want binary variables to remain binary etc.
Since there are more than 300 variables in the dataset, I do not want
to do this one-by-one, finding out whether var4 consists only of
integers between 1 and 10, var231 is binary etc.
So one way to go ist just take more of the same observations. E.g.,
obs 201 is the same as obs 133, obs 202 is the same as obs 78 and so
on. Or, and that's the other thing I was thinking of, I could create
obs 201 by drawing a new value from the existing distribution (given
by obs1-obs200) of var1, then drawing a new value from the
distribution of var2 and so on...
Davide
2009/6/22 Martin Weiss <[email protected]>:
>
> <>
>
> Davide said that he wanted to keep "the same (unknown to me) data
generating
> process". Every advice so far has assumed that this means that he just
wants
> "more of the same observations". If that was the case, he could also use a
> random frequency weight for every observation which would reduce the size
of
> his dataset.
>
> Davide could clarify whether he merely wants to duplicate observations
> randomly or whether he really wants "new" observations...
>
>
>
> HTH
> Martin
>
>
> -----Ursprüngliche Nachricht-----
> Von: [email protected]
> [mailto:[email protected]] Im Auftrag von Austin
Nichols
> Gesendet: Montag, 22. Juni 2009 14:33
> An: [email protected]
> Betreff: Re: st: Bootstrapping new observations to add to an existing
> dataset
>
> Davide Cantoni <[email protected]> :
> You don't say how many more obs you want--let's assume you want about
> 100 times as many:
>
> expand 100
>
> will do it, or
>
> g u=round(uniform()*200)
> expand u
>
> for a random-sized sample about 100 times as big with the same DGP.
> You could also
>
> loc n=_N*100
> g u=round(uniform()*1000)
> expand u
> drop u
> g u=uniform()
> sort u
> drop if _n>`n'
>
> for a sample 100 times as big but with random numbers of replications
> of each obs.
>
> On Sun, Jun 21, 2009 at 11:58 PM, Davide Cantoni
> <[email protected]> wrote:
>>
>> Hello, I am stuck while thinking about this issue and I would
>> appreciate your suggestions. I have a dataset which I use for
>> simulation purposes, to test whether my do-files run correctly. The
>> issue is that this dataset is too short for many applications, as it
>> has only 200 observations.
>>
>> What I want to do is expand this dataset to include more observations,
>> but keeping the same (unknown to me) data generating process that
>> created the first 200 observations. So I was thinking to proceed in a
>> bootstrapping manner, by drawing the values for each one of the
>> variables (var1, var2 etc etc) for the new observations from the
>> empirical distributions of var1, var2,... in the first 200
>> observations. Yet, I have no idea on how to implement this. I'm
>> grateful for any idea. Thanks for your interest,
>>
>> Davide
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