vslSSEditOutliersDetection

Developer Reference for Intel® oneAPI Math Kernel Library for C

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ID 766684

Date 12/16/2022

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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.h

Input Parameters

Name	Type	Description
task	VSLSSTaskPtr	Descriptor of the task
nparams	const MKL_INT*	Pointer to the number of method parameters
params	const float* for vslsSSEditOutliersDetection const double* for vsldSSEditOutliersDetection	Pointer to the array of method parameters
w	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.

Output Parameters

Name	Type	Description
status	int	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® oneAPI Math Kernel Library 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 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® oneAPI Math Kernel Library Summary Statistics Application Notes document [SS Notes].

Parent topic: Summary Statistics Task Editors

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Developer Reference for Intel® oneAPI Math Kernel Library for C

vslSSEditOutliersDetection