The structure
contains the uni/bi-variate data to be thinned on the
basis of a probability density estimate.
Data Types: single| double
Specify optional comma-separated pairs of Name,Value
arguments.
Name
is the argument name and Value
is the corresponding value. Name
must appear
inside single quotes (' '
).
You can specify several name and value pair arguments in any order as
Name1,Value1,...,NameN,ValueN
.
Example:
bandwidth,0.35
, support,'positive'
, cup, 0.8
, pstar, 0.95
, 'method','comp2one'
The bandwidth used to estimate
the density. It can be estimated from the data using
function bwe.
Example: bandwidth,0.35
Data Types: scalar
The support of the density
estimation step. It can be 'unbounded' (the default) or
'positive' if the data are left-truncated with long right
tails. In the latter case, the option performs the density
estimate in the log domain and then transform the result
back. The theoretical rationale is that when kernel
density is applied to positive data, it does not yield
proper PDFs.
Example: support,'positive'
Data Types: char
The upper limit for the pdf used
to compute the retantion probability. If cup = 1
(default), no upper limit is set.
Example: cup, 0.8
Data Types: scalar
Probability with each a unit
enters in the thinning procedure. If pstar = 1 (default), all units
enter in the thinning procedure.
Example: pstar, 0.95
Data Types: scalar
The function used to retain the
observations. It can be:
- 'inverse' , i.e. (1 ./ pdfe) / max((1 ./ pdfe)))
- 'comp2one' (default), i.e. 1 - pdfe/max(pdfe))
Example: 'method','comp2one'
Data Types: char