EL.Huber | 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.

`EL.means`

X <- rnorm(100) Y <- rnorm(100) t.test(X, Y) EL.means(X, Y) EL.Huber(X, Y)

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