| MDEstimator {distrMod} | R Documentation |
The function MDEstimator provides a general way to compute
minimum distance estimates.
MDEstimator(x, ParamFamily, distance = KolmogorovDist, dist.name,
startPar = NULL, Infos, trafo = NULL,
penalty = 0, asvar.fct, ...)
x |
(empirical) data |
ParamFamily |
object of class "ParamFamily" |
distance |
(generic) function: to compute distance beetween (emprical)
data and objects of class "Distribution". |
dist.name |
optional name of distance |
startPar |
initial information used by optimize resp. optim;
i.e; if (total) parameter is of length 1, startPar is
a search interval, else it is an initial parameter value; if NULL
slot startPar of ParamFamily is used to produce it;
in the multivariate case, startPar may also be of class Estimate,
in which case slot untransformed.estimate is used. |
Infos |
character: optional informations about estimator |
trafo |
an object of class MatrixorFunction – a transformation
for the main parameter |
penalty |
(non-negative) numeric: penalizes non valid parameter-values |
asvar.fct |
optionally: a function to determine the corresponding
asymptotic variance; if given, asvar.fct takes arguments
L2Fam((the parametric model as object of class L2ParamFamily))
and param (the parameter value as object of class
ParamFamParameter); arguments are called by name; asvar.fct
may also process further arguments passed through the ... argument |
... |
further arguments to criterion or optimize
or optim, respectively. |
The argument distance has to be a (generic) function with arguments
the empirical data as well as an object of class "Distribution"
and possibly ...; e.g. KolmogorovDist (default),
TotalVarDist or HellingerDist. Uses mceCalc
for method dispatch.
An object of S4-class "MCEstimate" which inherits from class
"Estimate".
Matthias Kohl Matthias.Kohl@stamats.de,
Peter Ruckdeschel Peter.Ruckdeschel@itwm.fraunhofer.de
Huber, P.J. (1981) Robust Statistics. New York: Wiley.
Rieder, H. (1994) Robust Asymptotic Statistics. New York: Springer.
ParamFamily-class, ParamFamily,
MCEstimator, \code{MCEstimate-class},
fitdistr
## (empirical) Data
x <- rgamma(50, scale = 0.5, shape = 3)
## parametric family of probability measures
G <- GammaFamily(scale = 1, shape = 2)
## Kolmogorov(-Smirnov) minimum distance estimator
MDEstimator(x = x, ParamFamily = G, distance = KolmogorovDist)
## von Mises minimum distance estimator with default mu
MDEstimator(x = x, ParamFamily = G, distance = CvMDist)
## von Mises minimum distance estimator with default mu
MDEstimator(x = x, ParamFamily = G, distance = CvMDist,
asvar.fct = distrMod:::.CvMMDCovariance)
#*** variance routine is still in testing phase so not yet
#*** exported to namespace
## von Mises minimum distance estimator with mu = N(0,1)
MDEstimator(x = x, ParamFamily = G, distance = CvMDist, mu = Norm())
## Total variation minimum distance estimator
## gamma distributions are discretized
MDEstimator(x = x, ParamFamily = G, distance = TotalVarDist)
## or smoothing of emprical distribution (takes some time!)
#MDEstimator(x = x, ParamFamily = G, distance = TotalVarDist, asis.smooth.discretize = "smooth")
## Hellinger minimum distance estimator
## gamma distributions are discretized
distroptions(DistrResolution = 1e-10)
MDEstimator(x = x, ParamFamily = G, distance = HellingerDist, startPar = c(1,2))
distroptions(DistrResolution = 1e-6) # default
## or smoothing of emprical distribution (takes some time!)
#MDEstimator(x = x, ParamFamily = G, distance = HellingerDist, asis.smooth.discretize = "smooth")