Modifies array pointers related to multivariate outliers detection.
FORTRAN:
status = vslssseditoutliersdetection(task, nparams, params, w)
status = vsldsseditoutliersdetection(task, nparams, params, w)
C:
status = vslsSSEditOutliersDetection(task, nparams, params, w);
status = vsldSSEditOutliersDetection(task, nparams, params, w);
Name |
Type |
Description |
|---|---|---|
task |
Fortran: TYPE(VSL_SS_TASK) C: VSLSSTaskPtr |
Descriptor of the task |
nparams |
Fortran: INTEGER C: MKL_INT* |
Pointer to the number of method parameters |
params |
Fortran: REAL(KIND=4) DIMENSION(*) for vslssseditoutliersdetection REAL(KIND=8) DIMENSION(*) for vsldsseditoutliersdetection C: float* for vslsSSEditOutliersDetection double* for vsldSSEditOutliersDetection |
Pointer to the array of method parameters |
w |
Fortran: REAL(KIND=4) DIMENSION(*) for vslssseditoutliersdetection REAL(KIND=8) DIMENSION(*) for vsldsseditoutliersdetection C: float* for vslsSSEditOutliersDetection double* for vsldSSEditOutliersDetection |
Pointer to an array of size n. The array holds the weights of observations to be marked as outliers. |
Name |
Type |
Description |
|---|---|---|
status |
Fortran: INTEGER C: int |
Current status of the task |
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 an input parameter is NULL, the corresponding parameter in the task descriptor remains 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.
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 χ2 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) = 0 if i-th observation is detected as an outlier. Otherwise BaconWeights(i) = w(i), where w is the vector of input weights. If you do not provide the vector of input weights, BaconWeights(i) is set to 1 if the i-th observation is not detected as an outlier.
See additional details about usage model of the algorithm in the Intel(R) MKL Summary Statistics Library Application Notes document on the Intel® MKL web page.