ind.dijs

Utility function for Epidemic Algorithm.

internal

Algorithms for multivariate outlier detection when missing values occur.
Algorithms are based on Mahalanobis distance or data depth. Imputation is based
on the multivariate normal model or uses nearest neighbour donors. The algorithms
take sample designs, in particular weighting, into account. The methods are
described in Bill and Hulliger (2016) <doi:10.17713/ajs.v45i1.86>.

Martin Sterchi

modi

Multivariate Outlier Detection and Imputation for Incomplete
Survey Data

Beat Hulliger

ind.dijs function

<dl><dt>i</dt>
<dd>index i.</dd>
<dt>js</dt>
<dd>indexes js.</dd>
<dt>n</dt>
<dd>number of rows.</dd></dl>

Arguments

Author

Addressing function for Epidemic Algorithm — ind.dijs

<dl>

<dt>i</dt>
<dd>index i.</dd>


<dt>js</dt>
<dd>indexes js.</dd>


<dt>n</dt>
<dd>number of rows.</dd>

</dl>

ind.dijs: Addressing function for Epidemic Algorithm

Description

Usage

Arguments

Author