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Re: st: Multilevel models and multiple imputation


From   "Stas Kolenikov" <skolenik@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Multilevel models and multiple imputation
Date   Tue, 11 Nov 2008 15:05:36 -0600

This has very little to do with multiple imputation or multilevel
models. This is work for -nlcom-, and using anything else is likely an
inferior solution:

nlcom exp(_b[lns1_1_1:_cons])

As it was mentioned, exponentiation is a pretty brutal operation
skewing the distributions quite markedly. There might be hope for
asymptotic normality of the logged variances; there is less hope for
asymptotic normality of the variances themselves, hence an additional
reason for log transformation.

On 11/11/08, Nailing Xia <nailingxia@gmail.com> wrote:
> Hi,
>
>  I have a question regarding the standard error of the random-effects
>  parameter in multi-level models (-xtmixed).
>
>  I am estimating a multilevel model using -xtmixed-, and the dataset I
>  use is from multiple imputation procedure using -ice-. I use -mim- for
>  estimation and the command is : mim: xtmixed irtm pexpect pbelmth
>  mclsize mtexp if [parwgt] || schid: || childid:. The output gives the
>  random-effects paramters in logrithm of the paramters:
>  ------------------------------------------------------------------------------
>         irtm |     Coef.  Std. Err.        [95% Conf. Int.]   MI.df
>  -------------+----------------------------------------------------------------
>    /lns1_1_1 |   1.20731   .052497                    1.10429  1.31034  923.0
>    /lns2_1_1 |   2.42047   .009313                    2.40208  2.43886  164.9
>     /lnsig_e   |   2.21744    .00496                     2.2077
>  2.22718  597.9
>  ------------------------------------------------------------------------------
>
>  I know I can get the paramter by using di exp(1.20731), but how can I
>  get the corresponding standard errors?



-- 
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.
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