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Parameter Estimation and Hypothesis Testing of Geographically Weighted Multivariate Generalized Poisson Regression

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  • Sarni Maniar Berliana

    (Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
    Department of Statistics, Politeknik Statistika STIS, Jakarta 13330, Indonesia
    These authors contributed equally to this work.)

  • Purhadi

    (Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
    These authors contributed equally to this work.)

  • Sutikno

    (Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
    These authors contributed equally to this work.)

  • Santi Puteri Rahayu

    (Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
    These authors contributed equally to this work.)

Abstract

We introduce a new multivariate regression model based on the generalized Poisson distribution, which we called geographically-weighted multivariate generalized Poisson regression (GWMGPR) model, and we present a maximum likelihood step-by-step procedure to obtain parameters for it. We use the maximum likelihood ratio test to examine the significance of the regression parameters and to define their critical region.

Suggested Citation

  • Sarni Maniar Berliana & Purhadi & Sutikno & Santi Puteri Rahayu, 2020. "Parameter Estimation and Hypothesis Testing of Geographically Weighted Multivariate Generalized Poisson Regression," Mathematics, MDPI, vol. 8(9), pages 1-14, September.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:9:p:1523-:d:409822
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    References listed on IDEAS

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    2. M. Fathurahman & Purhadi & Sutikno & Vita Ratnasari, 2020. "Geographically Weighted Multivariate Logistic Regression Model and Its Application," Abstract and Applied Analysis, Hindawi, vol. 2020, pages 1-10, August.
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    4. Felix Famoye, 2010. "A new bivariate generalized Poisson distribution," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 64(1), pages 112-124, February.
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    7. Felix Famoye, 2015. "A Multivariate Generalized Poisson Regression Model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(3), pages 497-511, February.
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