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自己下载robreg 命令看看是不是
help robreg
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Title
robreg -- Robust regression
Syntax
MM-estimator
robreg mm depvar varlist [if] [in] [, mm_options ]
M-estimator
robreg m depvar [varlist] [if] [in] [, m_options ]
S-estimator
robreg s depvar varlist [if] [in] [, s_options ]
LMS/LQS/LTS-estimator
robreg lms depvar varlist [if] [in] [, lqs_options ]
robreg lqs depvar varlist [if] [in] [, lqs_options ]
robreg lts depvar varlist [if] [in] [, lqs_options ]
Replay syntax
robreg [, level(#) ]
mm_options description
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Main
efficiency(#) gaussian efficiency; # in 70(5)95; default is efficiency(85)
bp(#) breakdown point; # in .10(.05).50; default is bp(0.5)
Biweight M-estimate
k(#) tuning constant; not allowed with efficiency()
tolerance(#) tolerance for IRWLS weights; default is tolerance(1e-6)
iterate(#) maximum number of iterations; default is iterate(16000)
relax continue even if convergence not reached
generate(newvar) store IRWLS weights
replace overwrite existing variable
Initial S-estimate
nsamp(#) number of trial samples
sopts(s_options) additional options passed through to S-algorithm
save(name) save S-estimate
Standard errors
vce(norobust) traditional standard errors
norobust synonym for vce(norobust)
Reporting
level(#) set confidence level; default is level(95)
first display initial S-estimate
nodots suppress progress dots of S-estimate
log display RWLS iteration log
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m_options description
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Main
huber use Huber objective function; the default
biweight use biweight objective function; bisquare is a synonym
efficiency(#) gaussian efficiency; # in 70(5)95; default is efficiency(95)
k(#) tuning constant; not allowed with efficiency()
IRWLS algorithm
tolerance(#) tolerance for IRWLS weights; default is tolerance(1e-6)
iterate(#) maximum number of iterations; default is iterate(16000)
relax continue even if convergence not reached
generate(newvar) store IRWLS weights
replace overwrite existing variable
Initial estimate
init(arg) initial estimate; arg may be lav, ols, name, or .; default is init(lav)
save(name) save initial estimate
Scale estimate
scale(#) provide preliminary scale estimate
updatescale update scale estimate in each iteration
center center residuals when computing scale
Standard errors
vce(norobust) traditional standard errors
vce(pv) traditional standard errors using pseudo-values approach
norobust synonym for vce(norobust)
nose skip computation of standard errors
Reporting
level(#) set confidence level; default is level(95)
first display initial estimate
log display RWLS iteration log
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s_options description
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Main
bp(#) breakdown point; # in .10(.05).50; default is bp(0.5)
k(#) tuning constant; not allowed with bp()
Resampling algorithm
nsamp(#) number of trial samples
alpha(#) maximum risk of bad solution; default is alpha(0.01)
epsilon(#) maximum contamination fraction; default is epsilon(0.2)
nkeep(#) number of candidates to keep; default is nkeep(2)
rsteps(#) number of local improvement steps; default is rsteps(1)
stolerance(#) tolerance for scale estimate; default is stolerance(1e-6)
siterate(#) maximum number of iterations for scale estimate; default is
siterate(16000)
tolerance(#) tolerance for coefficient vector; default is tolerance(1e-6)
iterate(#) maximum number of RWLS iterations; default is iterate(16000)
ssteps(#) number of scale approximation steps; default is ssteps(1)
generate(newvar) store IRWLS weights
replace overwrite existing variable
Standard errors
vce(norobust) traditional standard errors
norobust synonym for vce(norobust)
nose skip computation of standard errors
Reporting
level(#) set confidence level; default is level(95)
nodots suppress progress dots
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lqs_options description
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Main
* bp(#) breakdown point; # in (0,0.5]; default is bp(0.5)
Resampling algorithm
nsamp(#) number of trial samples
alpha(#) maximum risk of bad solution; default is alpha(0.01)
epsilon(#) maximum contamination fraction; default is epsilon(0.2).
generate(newvar) store minimizing sample
replace overwrite existing variable
Reporting
nodots suppress progress dots
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* bp() is not allowed with robreg lms
Description
robreg provides a number of robust estimators for linear regression models. The command
accompanies Jann (2010), a survey paper on robust regression in a German handbook on social
science data analysis.
robreg mm fits the efficient high breakdown MM-estimator proposed by Yohai (1987). On the
first stage, a high breakdown S-estimator is applied to estimate the residual scale and derive
starting values for the coefficients vector. On the second stage, an efficient bisquare
M-estimator is applied to obtain the final coefficient estimates.
robreg m fits regression M-estimators (Huber 1973) using iteratively reweighted least squares
(IRWLS).
robreg s fits the high breakdown S-estimator introduced by Rousseeuw and Yohai (1984) using
the fast algorithm proposed by Salibian-Barrera and Yohai (2006).
robreg lms, robreg lqs, and robreg lts fit the least median of squares (LMS), least quantile
of squares (LQS; a generalization of LMS), and the least trimmed squares (LTS) estimators
(Rousseeuw and Leroy 1987). Estimation is carried out using simple resampling without local
improvement (e.g. Rousseeuw and Leroy 1987:197). Computation of standard errors is not
supported for LMS, LQS, and LTS.
For a recent contribution of similar estimators in Stata also see Verardi and Croux (2009).
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