WH.mat.sum

WH.mat.sum,MatH,MatH-method

WH.mat.sum,MatH-method

It sums two <code>MatH</code> objects, i.e. two matrices of distributions,
by summing the quantile functions of histograms. This sum is consistent with
a set of distributions equipped with a L2 wasserstein metric.

In the framework of Symbolic Data Analysis, a relatively new
approach to the statistical analysis of multi-valued data, we consider
histogram-valued data, i.e., data described by univariate histograms. The
methods and the basic statistics for histogram-valued data are mainly based
on the L2 Wasserstein metric between distributions, i.e., the Euclidean metric
between quantile functions. The package contains unsupervised classification
techniques, least square regression and tools for histogram-valued data and for
histogram time series. An introducing paper is Irpino A. Verde R. (2015) <doi: 10.1007/s11634-014-0176-4>.

Antonio Irpino

HistDAWass

Histogram-Valued Data Analysis

WH.mat.sum function

<dl><dt>object1</dt>
<dd>a <code>MatH</code> object</dd>
<dt>object2</dt>
<dd>a <code>MatH</code> object</dd></dl>

Arguments

Method WH.mat.sum — WH.mat.sum

<dl>

<dt>object1</dt>
<dd>a <code>MatH</code> object</dd>


<dt>object2</dt>
<dd>a <code>MatH</code> object</dd>

</dl>

WH.mat.sum: Method WH.mat.sum

Description

Usage

Value

Arguments

Examples