psi.lismi

Defined in Little and Smith for ER 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

psi.lismi function

<dl><dt>d</dt>
<dd>vector of distances.</dd>
<dt>present</dt>
<dd>present.</dd>
<dt>psi.par</dt>
<dd>parameters.</dd></dl>

Arguments

Author

psi-function — psi.lismi

<dl>

<dt>d</dt>
<dd>vector of distances.</dd>


<dt>present</dt>
<dd>present.</dd>


<dt>psi.par</dt>
<dd>parameters.</dd>

</dl>

psi.lismi: psi-function

Description

Usage

Arguments

Author