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ABSTRACT: <br /> <br /> <br /> <br /> <br /> Counting process is a stochastic process which has a discrete state space and parameter index such as time, location and both of them. The example of counting process is point processes. There 4 approaches to construct p...

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Main Author: Cahyandari (NIM 20105006), Rini
Format: Theses
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/6685
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:6685
spelling id-itb.:66852017-09-27T14:41:44Z#TITLE_ALTERNATIVE# Cahyandari (NIM 20105006), Rini Indonesia Theses INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/6685 ABSTRACT: <br /> <br /> <br /> <br /> <br /> Counting process is a stochastic process which has a discrete state space and parameter index such as time, location and both of them. The example of counting process is point processes. There 4 approaches to construct point processes; counting measure, step function, sequence of points, and sequence of intervals. Related to the counting measure, trivial case of point processes is Poisson process, especially extended nonhomogeneous Poisson, which is characterized by an intensity function. The ilustration of intensity function is approached by analyzing maximum likelihood method, which nonhomogeneous Poisson likelihood function contains Riemann-Stieltjes integration and by using longleaf pine data at Wade Tract forest, Thomas County, Georgia. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description ABSTRACT: <br /> <br /> <br /> <br /> <br /> Counting process is a stochastic process which has a discrete state space and parameter index such as time, location and both of them. The example of counting process is point processes. There 4 approaches to construct point processes; counting measure, step function, sequence of points, and sequence of intervals. Related to the counting measure, trivial case of point processes is Poisson process, especially extended nonhomogeneous Poisson, which is characterized by an intensity function. The ilustration of intensity function is approached by analyzing maximum likelihood method, which nonhomogeneous Poisson likelihood function contains Riemann-Stieltjes integration and by using longleaf pine data at Wade Tract forest, Thomas County, Georgia.
format Theses
author Cahyandari (NIM 20105006), Rini
spellingShingle Cahyandari (NIM 20105006), Rini
#TITLE_ALTERNATIVE#
author_facet Cahyandari (NIM 20105006), Rini
author_sort Cahyandari (NIM 20105006), Rini
title #TITLE_ALTERNATIVE#
title_short #TITLE_ALTERNATIVE#
title_full #TITLE_ALTERNATIVE#
title_fullStr #TITLE_ALTERNATIVE#
title_full_unstemmed #TITLE_ALTERNATIVE#
title_sort #title_alternative#
url https://digilib.itb.ac.id/gdl/view/6685
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