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)
A list of class 'htest' containing the following components:
the empirical likelihood estimate for the difference of two smoothed Huber estimators.
a confidence interval for the difference of two smoothed Huber estimators.
the p-value for the test.
the value of the test statistic.
the character string 'Empirical likelihood smoothed Huber estimator difference test'.
the specified hypothesized value of the mean difference 'mu' under the null hypothesis.
a character string giving the names of the data.
a vector of data values.
a vector of data values.
a number specifying the null hypothesis.
confidence level of the interval.
the scale estimate of sample 'X'.
the scale estimate of sample 'Y'.
the asymptotic variance of initial (nonsmooth) Huber estimator for the sample 'X'.
the asymptotic variance of initial (nonsmooth) Huber estimator for the sample 'Y'.
tuning parameter for the Huber estimator.
E. Cers, J. Valeinis
A common choice for a robust scale estimate (parameters scaleX and scaleY) is the mean absolute deviation (MAD).
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.
EL.means
X <- rnorm(100)
Y <- rnorm(100)
t.test(X, Y)
EL.means(X, Y)
EL.Huber(X, Y)
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