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>.

Beat Hulliger

modi

Multivariate Outlier Detection and Imputation for Incomplete
Survey Data

Martin Sterchi

Tobias Schoch

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