Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/L2RegTypeFamily.R
Generates an object of class "RegTypeFamily".
| 1 2 3 4 5 6 7 8 9 10 11 | L2RegTypeFamily(name, distribution = LMCondDistribution(), distrSymm, 
        main = 0, nuisance, trafo, param, props = character(0), 
        L2deriv = EuclRandVarList(EuclRandVariable(
                  Map = list(function(x) {x[1] * x[2]}),
                  Domain = EuclideanSpace(dimension = 2),
                  dimension = 1)),
        ErrorDistr = Norm(), ErrorSymm, RegDistr = Norm(), RegSymm, 
        Regressor = RealRandVariable(Map = list(function(x) {x}), Domain = Reals()), 
        ErrorL2deriv = EuclRandVarList(RealRandVariable(Map = list(function(x) {x}), 
                                                        Domain = Reals())), 
        ErrorL2derivSymm, ErrorL2derivDistr, ErrorL2derivDistrSymm, FisherInfo)
 | 
| name | name of the family | 
| distribution | conditional distribution (given the regressor) | 
| distrSymm |  symmetry of  | 
| ErrorDistr | error distribution | 
| ErrorSymm |  object of class  | 
| main | main parameter | 
| nuisance | optional nuisance parameter | 
| trafo | matrix: optional transformation of the parameter | 
| param | parameter of the family | 
| props | properties of the family | 
| RegDistr | regressor distribution | 
| RegSymm |  object of class  | 
| Regressor | regressor | 
| L2deriv |  object of class  | 
| ErrorL2deriv |  object of class  | 
| ErrorL2derivDistr |  distribution of  | 
| ErrorL2derivSymm |  object of class  | 
| ErrorL2derivDistrSymm |  object of class  | 
| FisherInfo | Fisher information matrix | 
If name is missing, the default
“L2 differentiable regression type family” is used.
If param is missing, the parameter is created via
main, nuisance and trafo as described
in ParamFamParameter. In case distrSymm, 
ErrorSymm, RegSymm is missing, they are
set to NoSymmetry(). If FisherInfo is missing,
it is computed via numerical integration.
Object of class "L2RegTypeFamily"
Matthias Kohl Matthias.Kohl@stamats.de
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.
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