<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF xmlns="http://purl.org/rss/1.0/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/">
<channel rdf:about="http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/448">
<title>Jos M K</title>
<link>http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/448</link>
<description/>
<items>
<rdf:Seq>
<rdf:li rdf:resource="http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/449"/>
</rdf:Seq>
</items>
<dc:date>2026-04-29T02:29:05Z</dc:date>
</channel>
<item rdf:about="http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/449">
<title>HARRIS PROCESSES</title>
<link>http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/449</link>
<description>HARRIS PROCESSES
Sebastian, Sherly; Jos, M. K; Sandhya, E; Raju, N
In this paper, we develop two stochastic models where the variable under consideration&#13;
follows Harris distribution. The mean and variance of the processes are derived and the&#13;
processes are shown to be non-stationary. In the second model, starting with a Poisson&#13;
process, an alternate way of obtaining Harris process is introduced.
</description>
<dc:date>2005-11-01T00:00:00Z</dc:date>
</item>
</rdf:RDF>
