Poisson and Logistic Regressions for Inhomogeneous Multivariate Point Processes: A Case Study in the Barro Colorado Island Plot

Ahmad Husain*, Achmad Choiruddin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

This study aims to extend the estimating equations based on the Poisson and logistic regression likelihoods to model the intensity of a multivariate point process. The proposed approaches result in a framework equivalent to the estimation procedure for generalized linear model. The estimation is different from the existing methods where repetition independently with respect to the number of types of point process is obliged. Our approach does not require repetition and hence could be computationally faster. We implement our method to analyze the distribution of 9-species of trees in the Barro Colorado Island rainforest with respect to 11-environmental variables.

Original languageEnglish
Title of host publicationSoft Computing in Data Science - 6th International Conference, SCDS 2021, Proceedings
EditorsAzlinah Mohamed, Bee Wah Yap, Jasni Mohamad Zain, Michael W. Berry
PublisherSpringer Science and Business Media Deutschland GmbH
Pages301-311
Number of pages11
ISBN (Print)9789811673337
DOIs
Publication statusPublished - 2021
Event6th International Conference on Soft Computing in Data Science, SCDS 2021 - Virtual, Online
Duration: 2 Nov 20213 Nov 2021

Publication series

NameCommunications in Computer and Information Science
Volume1489 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference6th International Conference on Soft Computing in Data Science, SCDS 2021
CityVirtual, Online
Period2/11/213/11/21

Keywords

  • Logistic regression
  • Multivariate point pattern
  • Poisson regression

Fingerprint

Dive into the research topics of 'Poisson and Logistic Regressions for Inhomogeneous Multivariate Point Processes: A Case Study in the Barro Colorado Island Plot'. Together they form a unique fingerprint.

Cite this