TML1.noncensored: Truncated Maximum Likelihood Estimates of Location and Scale

Description Usage Arguments Value References See Also Examples

Description

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.

Usage

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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(), ...)

Arguments

y

Observation vector

errors
  • "Gaussian": the error distribution is assumed to be approximately Gaussian.

  • "logWeibull" : the error distribution is assumed to be approximately log-Weibull.

cu

Preliminary minimal upper cut-off. The default is 2.5 in the Gaussian case and 1.855356 in the log-Weibull case.

initial
  • "S" : initial S-estimate.

  • "input" : the initial estimate is given on input.

otp
  • "adaptive": adaptive cut-off.

  • "fixed" : non adaptive cut-off.

cov
  • "no": no estimate of the covariance matrix of the estimates is provided on output.

  • "parametric": a parametric estimate of the covariance matrix of the location-scale estimates is provided (to be used when n is small).

  • "nonparametric": a nonparametric estimate of the covariance matrix of the location-scale estimates is provided.

input

Initial input estimates of location and scale.
Required when initial="input".

  • "Gaussian case" : list(theta=...,sigma=...) initial input estimates. theta: location; sigma: scale.

  • "logWeibull case" : list(tau=...,v=...) initial input estimates of location (tau) and scale (v).

control

Control parameters. For the default values, see the function TML1.noncensored.control.

...

If initial="S", parameters for the computation of the initial S estimates. See the function TML1.noncensored.control.S for the default values.

Value

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).

References

Marazzi A., Yohai V. (2004). Adaptively truncated maximum likelihood regression with asymmetric errors. Journal of Statistical Planning and Inference, 122, 271-291.

See Also

TML.noncensored, TML1.noncensored.control, TML1.noncensored.control.S

Examples

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## 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)

RobustAFT documentation built on March 13, 2020, 1:31 a.m.