Modelling interactions in spatial point patterns parameter estimation by the maximus likelihood method

Markov point processes are a natural family of models for point patterns where the pattern formation is a consequence of interactions between points. The potential function or, alternatively, interaction function is a data summary in such situations. This study comprises models and their use in data...

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Bibliografiset tiedot
Päätekijä: Penttinen, Antti
Aineistotyyppi: Väitöskirja
Kieli:eng
Julkaistu: 1984
Aiheet:
Linkit: https://jyx.jyu.fi/handle/123456789/103816
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author Penttinen, Antti
author_facet Penttinen, Antti Penttinen, Antti
author_sort Penttinen, Antti
datasource_str_mv jyx
description Markov point processes are a natural family of models for point patterns where the pattern formation is a consequence of interactions between points. The potential function or, alternatively, interaction function is a data summary in such situations. This study comprises models and their use in data analysis. The main problem is the estimation of parametrized potential function from a mapped point pattern data through maximizing the likelihood function. The exact form of the likelihood function is not known for interaction models and therefore, the maximum likelihood estimation must be based on approximations. The emphasis of this study is in sparse data approximations and on the other hand in simulation-based (Monte Carlo) approximations of the likelihood function. The main result is the use of the efficient score stochastic process in the approximation of the maximum likelihood solution. The method is illustrated by an empirical example where interactions in waterstrider populations have been modelled.
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The potential function or, alternatively, interaction function is a data summary in such situations. This study comprises models and their use in data analysis. The main problem is the estimation of parametrized potential function from a mapped point pattern data through maximizing the likelihood function. The exact form of the likelihood function is not known for interaction models and therefore, the maximum likelihood estimation must be based on approximations. The emphasis of this study is in sparse data approximations and on the other hand in simulation-based (Monte Carlo) approximations of the likelihood function. The main result is the use of the efficient score stochastic process in the approximation of the maximum likelihood solution. 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spellingShingle Penttinen, Antti Modelling interactions in spatial point patterns : parameter estimation by the maximus likelihood method otanta tilastomenetelmät stokastiset prosessit parametrit tilastotiede matematiikka Monte Carlo -menetelmät Markovin ketjut
title Modelling interactions in spatial point patterns : parameter estimation by the maximus likelihood method
title_full Modelling interactions in spatial point patterns : parameter estimation by the maximus likelihood method
title_fullStr Modelling interactions in spatial point patterns : parameter estimation by the maximus likelihood method Modelling interactions in spatial point patterns : parameter estimation by the maximus likelihood method
title_full_unstemmed Modelling interactions in spatial point patterns : parameter estimation by the maximus likelihood method Modelling interactions in spatial point patterns : parameter estimation by the maximus likelihood method
title_short Modelling interactions in spatial point patterns
title_sort modelling interactions in spatial point patterns parameter estimation by the maximus likelihood method
title_sub parameter estimation by the maximus likelihood method
title_txtP Modelling interactions in spatial point patterns : parameter estimation by the maximus likelihood method
topic otanta tilastomenetelmät stokastiset prosessit parametrit tilastotiede matematiikka Monte Carlo -menetelmät Markovin ketjut
topic_facet Markovin ketjut Monte Carlo -menetelmät matematiikka otanta parametrit stokastiset prosessit tilastomenetelmät tilastotiede
url https://jyx.jyu.fi/handle/123456789/103816 http://www.urn.fi/URN:ISBN:978-952-86-0834-9
work_keys_str_mv AT penttinenantti modellinginteractionsinspatialpointpatternsparameterestimationbythemaximuslikelih