TML1.noncensored | R Documentation |
This functions computes the truncated maximum likelihood estimates of location and scale
described in Marazzi and Yohai (2004).
It assumes that the error distribution is approximately Gaussian or log-Weibull.
The cut-off values for outlier rejection are fixed or adaptive.
This function is a simplified version of TML.noncensored
for the case without covariates.
TML1.noncensored(y, errors= c("Gaussian", "logWeibull"), cu = NULL,
initial = c("S", "input"), otp = c("adaptive", "fixed"),
cov = c("no", "parametric", "nonparametric"), input = NULL,
control = list(), ...)
y |
Observation vector |
errors |
|
cu |
Preliminary minimal upper cut-off. The default is 2.5 in the Gaussian case and 1.855356 in the log-Weibull case. |
initial |
|
otp |
|
cov |
|
input |
Initial input estimates of location and scale.
|
control |
Control parameters. For the default values, see the function |
... |
If initial="S", parameters for the computation of the initial S estimates. See the function |
A list with the following components:
th0 |
Initial location estimate (S or input). |
v0 |
Initial scale estimate (S or input). |
nit0 |
Reached number of iteration if initial="S" |
th1 |
Final location estimate. |
v1 |
Final scale estimate. |
nit1 |
Reached iteration number in IRLS algorithm for final estimate (only for the log_Weibull case). |
tu, tl |
Final cut-off values. |
alpha |
Estimated proportion of retained observations. |
tn |
Number of retained observations. |
beta |
Consistency constant for scale. |
wi |
Vector of weights (0 for rejected observations, 1 for retained observations). |
CV0 |
Covariance matrix of the initial estimates (th0,v0). |
CV1 |
Covariance matrix of the final estimates (th1,v1). |
Marazzi A., Yohai V. (2004). Adaptively truncated maximum likelihood regression with asymmetric errors. Journal of Statistical Planning and Inference, 122, 271-291.
TML.noncensored
, TML1.noncensored.control
, TML1.noncensored.control.S
## Not run:
data(Z243)
Cost <- Z243$CouTot
y <- log(Cost)
ctrl <- TML1.noncensored.control(iv=1,tol=1e-3)
z <- TML1.noncensored(y,errors="logWeibull", initial="S",otp="adaptive",
cov="no",control=ctrl)
## End(Not run)
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