Description Usage Arguments Value Author(s) References See Also Examples
wle.normal
is used to robust estimate the location and the scale parameters via Weighted Likelihood, when the sample is iid from a normal distribution with unknown mean and variance.
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x |
a vector contain the observations. |
boot |
the number of starting points based on boostrap subsamples to use in the search of the roots. |
group |
the dimension of the bootstap subsamples. The default value is max(round(size/4),2) where size is the number of observations. |
num.sol |
maximum number of roots to be searched. |
raf |
type of Residual adjustment function to be use:
|
smooth |
the value of the smoothing parameter. |
tol |
the absolute accuracy to be used to achieve convergence of the algorithm. |
equal |
the absolute value for which two roots are considered the same. (This parameter must be greater than |
max.iter |
maximum number of iterations. |
verbose |
if |
wle.normal
returns an object of class
"wle.normal"
.
Only print method is implemented for this class.
The object returned by wle.normal
are:
location |
the estimator of the location parameter, one value for each root found. |
scale |
the estimator of the scale parameter, one value for each root found. |
residuals |
the residuals associated to each observation, one column vector for each root found. |
tot.weights |
the sum of the weights divide by the number of observations, one value for each root found. |
weights |
the weights associated to each observation, one column vector for each root found. |
f.density |
the non-parametric density estimation. |
m.density |
the smoothed model. |
delta |
the Pearson residuals. |
freq |
the number of starting points converging to the roots. |
call |
the match.call(). |
tot.sol |
the number of solutions found. |
not.conv |
the number of starting points that does not converge after the |
Claudio Agostinelli
Markatou, M., Basu, A. and Lindsay, B.G., (1998) Weighted likelihood estimating equations with a bootstrap root search, Journal of the American Statistical Association, 93, 740-750.
Agostinelli, C., (1998) Inferenza statistica robusta basata sulla funzione di verosimiglianza pesata: alcuni sviluppi, Ph.D Thesis, Department of Statistics, University of Padova.
wle.smooth an algorithm to choose the smoothing parameter for normal distribution and normal kernel.
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