EL.Huber {EL} | R Documentation |
Empirical likelihood inference for the difference of smoothed Huber estimators. This includes a test for the null hypothesis for a constant difference of smoothed Huber estimators, confidence interval and EL estimator.
EL.Huber(X, Y, mu = 0, conf.level = 0.95, scaleX=1, scaleY=1, VX = 2.046, VY = 2.046, k = 1.35)
X |
a vector of data values. |
Y |
a vector of data values. |
mu |
a number specifying the null hypothesis. |
conf.level |
confidence level of the interval. |
scaleX |
the scale estimate of sample 'X'. |
scaleY |
the scale estimate of sample 'Y'. |
VX |
the asymptotic variance of initial (nonsmooth) Huber estimator for the sample 'X'. |
VY |
the asymptotic variance of initial (nonsmooth) Huber estimator for the sample 'Y'. |
k |
tuning parameter for the Huber estimator. |
A common choice for a robust scale estimate (parameters scaleX and scaleY) is the mean absolute deviation (MAD).
A list of class 'htest' containing the following components:
estimate |
the empirical likelihood estimate for the difference of two smoothed Huber estimators. |
conf.int |
a confidence interval for the difference of two smoothed Huber estimators. |
p.value |
the p-value for the test. |
statistic |
the value of the test statistic. |
method |
the character string 'Empirical likelihood smoothed Huber estimator difference test'. |
null.value |
the specified hypothesized value of the mean difference 'mu' under the null hypothesis. |
data.name |
a character string giving the names of the data. |
E. Cers, J. Valeinis
J. Valeinis, E. Cers. Extending the two-sample empirical likelihood. To be published. Preprint available at http://home.lanet.lv/~valeinis/lv/petnieciba/EL_TwoSample_2011.pdf.
F. Hampel, C. Hennig and E. A. Ronchetti (2011). A smoothing principle for the Huber and other location M-estimators, Computational Statistics & Data Analysis, 55(1), 324-337.
X <- rnorm(100) Y <- rnorm(100) t.test(X, Y) EL.means(X, Y) EL.Huber(X, Y)