Density, cumulative distribution, quantiles and random number generator for Huber's least favourable distribution.

1 2 3 4 |

`x` |
vector of quantiles. Missing values ( |

`q` |
vector of quantiles. Missing values ( |

`p` |
vector of probabilities. Missing values ( |

`n` |
sample size. If |

`k` |
tuning constant. Values should preferably lie between 1 and 1.5. The default is 1.345, which gives 95% efficiency at the Normal. |

Inversion of the cumulative distribution function is used to generate deviates from Huber's least favourable distribution.

Density (`dHuber`

), probability (`pHuber`

),
quantile (`qHuber`

), or random sample (`rHuber`

)
for Huber's least favourable distribution with tuning constant
`k`

. If values are missing, `NA`

s will be returned.

The function `rHuber`

causes creation of the dataset
`.Random.seed`

if it does not already exist; otherwise its
value is updated.

Huber's least favourable distribution is a compound distribution
with gaussian behaviour in the interval (-`k`

,`k`

) and
double exponential tails. It is strongly related to Huber's
M-estimator, which represents the maximum likelihood estimator of
the location parameter.

Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J. and Stahel, W. A.
(1986) *Robust Statistics: The Approach Based on Influence
Functions*. New York: Wiley.

1 2 3 4 |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.