Developer Reference

  • 2020.2
  • 07/15/2020
  • Public Content
Contents

vslSSEditOutliersDetection

Modifies array pointers related to multivariate outliers detection.

Syntax

status
=
vslssseditoutliersdetection
(
task
,
nparams
,
params
,
w
)
status
=
vsldsseditoutliersdetection
(
task
,
nparams
,
params
,
w
)
Include Files
  • mkl_vsl.f90
Input Parameters
Name
Type
Description
task
TYPE(VSL_SS_TASK)
Descriptor of the task
nparams
INTEGER
Pointer to the number of method parameters
params
REAL(KIND=4) DIMENSION(*)
for
vslssseditoutliersdetection
REAL(KIND=8) DIMENSION(*)
for
vsldsseditoutliersdetection
Pointer to the array of method parameters
w
REAL(KIND=4) DIMENSION(*)
for
vslssseditoutliersdetection
REAL(KIND=8) DIMENSION(*)
for
vsldsseditoutliersdetection
Pointer to an array of size
n
. The array holds the weights of observations to be marked as outliers.
Output Parameters
Name
Type
Description
status
INTEGER
Current status of the task
Description
The
vslSSEditOutliersDetection
routine uses the parameters passed to replace
  • the pointers to the number of method parameters and to the array of the method parameters of size
    nparams
  • the pointer to the array that holds the calculated weights of the observations
If you pass a value of
NULL
for a specific input parameter, the value of that parameter in the task descriptor is unchanged.
Intel® MKL
provides the BACON algorithm ([ Billor00 ]) for the detection of multivariate outliers. Pack the parameters of the BACON algorithm into the
params
array and pass them into the editor. Table
"Structure of the Array of BACON Parameters"
describes the
params
structure.
Structure of the Array of BACON Parameters
Array Position
Algorithm Parameter
Description
0
Method to start the algorithm
The parameter takes one of the following possible values:
VSL_SS_METHOD_BACON_MEDIAN_INIT
, if the algorithm is started using the median estimate. This is the default value of the parameter.
VSL_SS_METHOD_BACON_MAHALANOBIS_INIT
, if the algorithm is started using the Mahalanobis distances.
1
α
One-tailed probability that defines the
(1 -
α
) quantile of
χ
2
distribution with
p
degrees of freedom. The recommended value is
α
/
n
, where
n
is the number of observations. By default, the value is 0.05.
2
δ
Stopping criterion; the algorithm is terminated if the size of the basic subset is changed less than
δ
. By default, the value is 0.005.
Output of the algorithm is the vector of weights,
BaconWeights
, such that
BaconWeights
(
i