<>
" Also it is not clear to me if the reported bias is a quantity that
adds to the standard error or must be subtracted from the standard
error in calculating the variance."
Not sure what you mean. The bias is the difference btw the coefficient
estimate of your chosen estimator and the mean of the bootstrap estimates.
The standard error is the standard deviation of those bootstrap estimates,
as seen in this code:
*************
sysuse auto, clear
//get bs estimates
bs, reps(200) saving(myfile, /*
*/ replace) seed(1020): /*
*/ reg pr we le he
//store coeffs in -local-s
foreach var of varlist weight length headroom{
loc `var'coeff=_b[`var']
}
//get saved file of bs estimates
u myfile, clear
//calculate bias and bs standard error
foreach var in weight length headroom{
qui su _b_`var'
di in red "Coeff `var', Bias (Mean of Bootstraps-Original): " /*
*/ r(mean)-``var'coeff' _n /*
*/ "Bootstrap se: " r(sd) _n
}
//compare to official results
estat boot, all
*************
HTH
Martin
-----Ursprüngliche Nachricht-----
Von: [email protected]
[mailto:[email protected]] Im Auftrag von Giulio Rizzoli
Gesendet: Samstag, 19. September 2009 17:17
An: [email protected]
Betreff: st: Bootstrap and p-values
This mail didn't go through twice
I try the third time after reading the FAQ instructions.
Dear all:
Many medical variables are correlated, so I'm using the bootstrap
program to verify the most
frequently extracted variables within a backward and forward algorithm.
In the example below Nyha4 is by far the most extracted variable and
therefore the most significant statistically.
I usually publish tables of the selected variables with their
confidence limits.
I imagine that the bias corrected confidence limits provided by
bootstrap are the most correct ones.
Nonetheless until now I have always published the normal confidence
limits, because the p-value can be easily controlled from reviewers,
using the formula to calculate the variance under the normality assumption:
(lnucl-lnlcl)/(2*1.96))^2 . // lnucl=log of upper C. L.
lnlcl=log of lower C. L.
I would like to know there is a method to calculate the p_value
starting from the BC confidence limits.
Also it is not clear to me if the reported bias is a quantity that
adds to the standard error or must be subtracted from the standard
error in calculating the variance.
The BC confidence limits are narrower. So i imagine that bias should
be subtracted from standard error because bias relates to the part of
measurement error that is casual.
Am I correct?
If it is so it is important to have a p-value for this estimate but
the calculation with the usual zeta= _b/_se assumes a normality
distribution. Is it valid in this setting ?
It woul'd be wonderful if someone has a routine to calculate the
different p-values starting from the ereturn list of the bootstrap
saved values or could show how to calculate BC related p-values.
. bootstrap "sw, pr(.1) pe(.05): stcox fsex AOI sonoreint age nyha4
CPRENF CPRESPF CPIABP" _b,reps(1000) dots
command: sw , pr(.1) pe(.05) : stcox fsex AOI sonoreint age
nyha4 CPRENF CPRESPF CPIABP
statistics: b_sesso = _b[fsex]
b_age = _b[age]
b_nyha4 = _b[nyha4]
............................................................................
....
>....
Bootstrap statistics Number of obs =
327
Replications =
1000
----------------------------------------------------------------------------
--
Variable | Reps Observed Bias Std. Err. [95% Conf. Interval]
-------------+--------------------------------------------------------------
--
b_fsex | 597 -.3829885 -.1357204 .138327 -.6546561 -.1113208
(N)
| -.8652664 -.3303381
(P)
| -.4775621 -.2981049
(BC)
| -1.328869 .6971875
(BC)
b_age | 550 .0824691 .0245982 .0309384 .021697 .1432412
(N)
| .0681392 .1776492
(P)
| -.1053623 .1156664
(BC)
b_nyha4 | 845 1.043275 .2214171 .3910648 .2757012 1.810849
(N)
| .652275 2.103901
(P)
| .5398257 1.679764
(BC)
----------------------------------------------------------------------------
--
Note: N = normal
P = percentile
BC =
bias-corrected
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