LDEstimator {RobExtremes} | R Documentation |
Function LDEstimator
provides a general way to compute
estimates for a given parametric family of probability measures
(with a scale and shape parameter) which
can be obtained by matching location and dispersion functionals
against empirical counterparts.
LDEstimator(x, loc.est, disp.est, loc.fctal, disp.fctal, ParamFamily, loc.est.ctrl = NULL, loc.fctal.ctrl=NULL, disp.est.ctrl = NULL, disp.fctal.ctrl=NULL, q.lo =1e-3, q.up=15, log.q =TRUE, name, Infos, asvar = NULL, nuis.idx = NULL, trafo = NULL, fixed = NULL, asvar.fct = NULL, na.rm = TRUE, ..., .withEvalAsVar = FALSE, vdbg = FALSE) medkMAD(x, ParamFamily, k=1, q.lo =1e-3, q.up=15, nuis.idx = NULL, trafo = NULL, fixed = NULL, asvar.fct = NULL, na.rm = TRUE, ..., .withEvalAsVar = FALSE, vdbg = FALSE) medkMADhybr(x, ParamFamily, k=1, q.lo =1e-3, q.up=15, KK = 20, nuis.idx = NULL, trafo = NULL, fixed = NULL, asvar.fct = NULL, na.rm = TRUE, ..., .withEvalAsVar = FALSE) medSn(x, ParamFamily, q.lo =1e-3, q.up=10, nuis.idx = NULL, trafo = NULL, fixed = NULL, asvar.fct = NULL, na.rm = TRUE, accuracy = 100, ..., .withEvalAsVar = FALSE) medQn(x, ParamFamily, q.lo =1e-3, q.up=15, nuis.idx = NULL, trafo = NULL, fixed = NULL, asvar.fct = NULL, na.rm = TRUE, ..., .withEvalAsVar = FALSE)
x |
(empirical) data |
ParamFamily |
an object of class |
loc.est |
a function expecting |
disp.est |
a function expecting |
loc.fctal |
a function expecting a distribution object as first argument; location functional. |
disp.fctal |
a function expecting a distribution object as first argument; dispersion functional; may only take non-negative values. |
loc.est.ctrl |
a list (or |
disp.est.ctrl |
a list (or |
loc.fctal.ctrl |
a list (or |
disp.fctal.ctrl |
a list (or |
k |
numeric; additional parameter for |
KK |
numeric; Maximal number of trials with different |
q.lo |
numeric; lower bound for search intervall in shape parameter. |
q.up |
numeric; upper bound for search intervall in shape parameter. |
log.q |
logical; shall the zero search be done on log-scale? |
name |
optional name for estimator. |
Infos |
character: optional informations about estimator |
asvar |
optionally the asymptotic (co)variance of the estimator |
nuis.idx |
optionally the indices of the estimate belonging to nuisance parameter |
fixed |
optionally (numeric) the fixed part of the parameter |
trafo |
an object of class |
asvar.fct |
optionally: a function to determine the corresponding
asymptotic variance; if given, |
na.rm |
logical: if |
accuracy |
numeric: argument to be passed on to |
... |
further arguments to be passed to location estimator and functional and dispersion estimator and functional. |
vdbg |
logical; if |
.withEvalAsVar |
logical: shall slot |
The arguments loc.est
, disp.est
(location and dispersion estimators)
have to be functions with first argument x
(a numeric vector with the
empirical data) and additional, optional individual arguments to be passed on
in the respective calls as lists loc.est.ctrl
, disp.est.ctrl
,
and global additional arguments through the ...
argument.
Similarly, arguments loc.fctal
, disp.fctal
(location and
dispersion functionals) have to be functions with first argument an
object of class UnivariateDistribution
, and additional, optional
individual arguments to be passed on
in the respective calls as lists loc.fctal.ctrl
, disp.fctal.ctrl
,
and global additional arguments again through the ...
argument.
Uses .LDMatch
internally.
An object of S4-class "Estimate"
.
The values for q.lo
and q.up
are a bit delicate and
have to be found, model by model, by try and error.
As a rule, medSn
is rather slow, as the evaluation of the Sn
functional is quite expensive. So if medSn
is the estimator of choice,
it pays off, for a given shape-scale family, to evaluate medSn
on a
grid of shape-values (with scale 1) and then to use an interpolation techniques
in a particular method to replace the default one for this shape-scale family.
As an example, we have done so for the GPD family.
Nataliya Horbenko nhorbenko@gmail.com,
Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de
P. Ruckdeschel, N. Horbenko (2011): Yet another breakdown point notion: EFSBP –illustrated at scale-shape models. ArXiv 1005.1480. To appear at Metrika. DOI: 10.1007/s00184-011-0366-4.
A. Marazzi and C. Ruffieux (1999): The truncated mean of asymmetric distribution. Computational Statistics and Data Analysis 32: 79-100 .
ParamFamily-class
, ParamFamily
,
Estimate-class
## (empirical) Data set.seed(123) x <- rgamma(50, scale = 0.5, shape = 3) ## parametric family of probability measures G <- GammaFamily(scale = 1, shape = 2) medQn(x = x, ParamFamily = G) medSn(x = x, ParamFamily = G, q.lo = 0.5, q.up = 4) ## not tested on CRAN because it takes time... ## without speedup for Sn: LDEstimator(x, loc.est = median, disp.est = Sn, loc.fctal = median, disp.fctal = getMethod("Sn","UnivariateDistribution"), ParamFamily = G, disp.est.ctrl = list(constant=1)) medkMAD(x = x, ParamFamily = G) medkMADhybr(x = x, ParamFamily = G) medkMAD(x = x, k=10, ParamFamily = G) ##not at all robust: LDEstimator(x, loc.est = mean, disp.est = sd, loc.fctal = E, disp.fctal = sd, ParamFamily = G)