doutlier

doutlier defines an outlier distribution (Surface of a
trapezium) and uses input parameters given in the main function kfino_fit()

A method for detecting outliers with a Kalman filter on impulsed
noised outliers and prediction on cleaned data. 'kfino' is a robust
sequential algorithm allowing to filter data with a large number of outliers.
This algorithm is based on simple latent linear Gaussian processes as in the
Kalman Filter method and is devoted to detect impulse-noised outliers. These
are data points that differ significantly from other observations. 'ML'
(Maximization Likelihood) and 'EM' (Expectation-Maximization algorithm)
algorithms were implemented in 'kfino'. The method is described in full
details in the following arXiv e-Print: <arXiv:2208.00961>.

Isabelle Sanchez

kfino

Kalman Filter for Impulse Noised Outliers

Bertrand Cloez

Benedicte Fontez

doutlier function

<dl><dt>y</dt>
<dd>numeric, point</dd>
<dt>K</dt>
<dd>numeric, constant value</dd>
<dt>expertMin</dt>
<dd>numeric, the minimal weight expected by the user</dd>
<dt>expertMax</dt>
<dd>numeric, the maximal weight expected by the user</dd></dl>

Arguments

doutlier defines an outlier distribution (Surface of a
trapezium) and uses input parameters given in the main function kfino_fit() — doutlier

<dl>

<dt>y</dt>
<dd>numeric, point</dd>


<dt>K</dt>
<dd>numeric, constant value</dd>


<dt>expertMin</dt>
<dd>numeric, the minimal weight expected by the user</dd>


<dt>expertMax</dt>
<dd>numeric, the maximal weight expected by the user</dd>

</dl>

doutlier: doutlier defines an outlier distribution (Surface of a trapezium) and uses input parameters given in the main function kfino_fit()

Description

Usage

Value

Arguments

Details

Examples