Generates an object of class `"Av1CondContIC"`

;
i.e., an influence curves *eta* of the form

*
eta = A x Lambda_f min(1, max(c(x)/(|Ax|Lambda_f), (c(x) + b)/(|Ax|Lambda_f)))*

with lower clipping function *c*, standardized bias *b* and
standardizing matrix *A*. *Lambda_f* stands for
the L2 derivative of the corresponding error distribution.

1 2 3 4 5 6 7 8 | ```
Av1CondTotalVarIC(name, CallL2Fam = call("L2RegTypeFamily"),
Curve = EuclRandVarList(RealRandVariable(
Map = list(function(x) {x[1] * x[2]}),
Domain = EuclideanSpace(dimension = 2))),
Risks, Infos, clipUp = Inf, stand = as.matrix(1),
clipLo = RealRandVariable(Map = list(function(x) {-Inf}),
Domain = EuclideanSpace(dimension = 1)),
lowerCase = NULL, neighborRadius = 0)
``` |

`name` |
object of class |

`CallL2Fam` |
object of class |

`Curve` |
object of class |

`Risks` |
object of class |

`Infos` |
matrix of characters with two columns
named |

`clipUp` |
positive real: standardized bias. |

`clipLo` |
object of class |

`stand` |
matrix: standardizing matrix. |

`lowerCase` |
optional constant for lower case solution. |

`neighborRadius` |
radius of the corresponding (unconditional) contamination neighborhood. |

Object of class `"Av1CondTotalVarIC"`

Matthias Kohl Matthias.Kohl@stamats.de

Rieder, H. (1994) *Robust Asymptotic Statistics*. New York: Springer.

Kohl, M. (2005) *Numerical Contributions to the Asymptotic Theory of Robustness*.
Bayreuth: Dissertation.

`CondIC-class`

, `Av1CondTotalVarIC-class`

1 2 | ```
IC1 <- Av1CondTotalVarIC()
IC1
``` |

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