wle.normal.multi {wle} | R Documentation |
wle.normal.multi
is used to robust estimate the location and the covariance matrix via Weighted Likelihood, when the sample is iid from a normal multivariate distribution with unknown means and variance matrix.
wle.normal.multi_function(x, boot=30, group, num.sol=1, raf="HD", smooth, tol=10^(-6), equal=10^(-3), max.iter=500)
x |
a matrix 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. |
wle.normal.multi
returns an object of class
"wle.normal.multi"
.
Only print method is implemented for this class.
The object returned by wle.normal.multi
are:
location |
the estimator of the location parameters, one vector for each root found. |
variance |
the estimator of the covariance matrix, one matrix 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. |
tot.sol |
the number of solutions found. |
call |
the match.call(). |
not.conv |
the number of starting points that does not converge after the max.iter iteration are reached. |
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.
library(wle) data(iris) smooth <- wle.smooth(dimension=4,costant=4, weight=0.5,interval=c(0.3,0.7)) x.data <- as.matrix(iris[iris[,5]=="virginica",1:4]) result <- wle.normal.multi(x.data,boot=20,group=21, num.sol=3,smooth=smooth$root) result result <- wle.normal.multi(x.data,boot=20,group=21, num.sol=1,smooth=smooth$root) barplot(result$weights,col=2,xlab="Observations", ylab="Weights",ylim=c(0,1), names.arg=seq(1:length(result$weights)))