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

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Soft Computing in Data Science (SCDS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1489))

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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.

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Acknowledgments

The first author is grateful for the financial support from Lembaga Pengelola Dana Pendidikan (LPDP) under registration no. 20200511301777. We also thank the three reviewers for the helpful comments.

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Authors and Affiliations

  1. Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia

    Ahmad Husain & Achmad Choiruddin

Authors
  1. Ahmad Husain
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  2. Achmad Choiruddin
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Editor information

Editors and Affiliations

  1. Universiti Teknologi MARA, Shah Alam, Malaysia

    Azlinah Mohamed

  2. Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

    Bee Wah Yap

  3. Universiti Teknologi MARA, Shah Alam, Malaysia

    Jasni Mohamad Zain

  4. University of Tennessee, Knoxville, TN, USA

    Michael W. Berry

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Husain, A., Choiruddin, A. (2021). Poisson and Logistic Regressions for Inhomogeneous Multivariate Point Processes: A Case Study in the Barro Colorado Island Plot. In: Mohamed, A., Yap, B.W., Zain, J.M., Berry, M.W. (eds) Soft Computing in Data Science. SCDS 2021. Communications in Computer and Information Science, vol 1489. Springer, Singapore. https://doi.org/10.1007/978-981-16-7334-4_22

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  • DOI: https://doi.org/10.1007/978-981-16-7334-4_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-7333-7

  • Online ISBN: 978-981-16-7334-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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