wle.normal {wle}R Documentation

Robust Estimation in the Normal Model

Description

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.

Usage

wle.normal_function(x, boot=30, group, num.sol=1, raf="HD",
                    smooth=0.003, tol=10^(-6), equal=10^(-3), 
                    max.iter=500)

Arguments

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.
num.sol maximum number of roots to be searched.
raf type of Residual adjustment function to be use:
raf="HD": Hellinger Distance RAF,
raf="NED": Negative Exponential Disparity RAF,
raf="SCHI2": Symmetric Chi-Squared Disparity RAF.
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 tol).
max.iter maximum number of iterations.

Value

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.
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.
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 max.iter iteration are reached.

Author(s)

Claudio Agostinelli

References

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.

See Also

wle.smooth an algorithm to choose the smoothing parameter for normal distribution and normal kernel.

Examples

library(wle)

data(cavendish)

result <- wle.normal(cavendish)

result

result <- wle.normal(cavendish,boot=20,num.sol=1)

barplot(result$weights,col=2,xlab="Observations",
        ylab="Weights",ylim=c(0,1),
        names.arg=seq(1:length(result$weights)))