NDLI: Zero Inflated Poisson and Geographically Weighted Zero-Inflated Poisson Regression Model : Application to Elephantiasis ( Filariasis ) Counts Data

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Zero Inflated Poisson and Geographically Weighted Zero-Inflated Poisson Regression Model : Application to Elephantiasis ( Filariasis ) Counts Data

Content Provider	Semantic Scholar
Author	Purhadi Dewi, Yuliani Setia Amaliana, Luthfatul
Copyright Year	2015
Abstract	Corresponding Author: Purhadi Department of Statistics, Faculty of MIPA, Sepuluh Nopember Institute of Technology Jl. Arief Rahman Hakim, Surabaya 60111 Indonesia Email: purhadi@statistika.its.ac.id Abstract: Poisson regression has been widely used for modeling counts data. Violation of equidispersion assumption can occur when there are excess of zeros of the data. For that condition we can use Zero-Inflated Poisson (ZIP) to analyze such data, resulting global parameter estimates. However spatial data from various locations have their own characteristics depend on their socio-cultural, geographical and economic conditions. In this paper, we first review the theoretical framework of Zero-Inflated Poisson (ZIP) and Geographically Weighted Zero Inflated Poisson (GWZIP) regression. We use Maximum Likelihood (MLE) method and EM algorithm to estimate the model parameters. The F test is used to compare the two models. Second, we fit these models to the number of filariasis case of East Java. In our case, there is the preponderance of zeros in the data set (65.79%). The results prove that the spatial dependence is absent, but there is weak spatial heterogeneity of the data (significance level α = 0.1). Based on F test, ZIP and GWZIP regression are not significantly different.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://thescipub.com/PDF/jmssp.2015.52.60.pdf
Language	English
Access Restriction	Open
Subject Keyword	Aneesur Rahman Prize for Computational Physics Arabic numeral 0 Email Estimated Expectation–maximization algorithm Filarial Elephantiases Filariasis Fractional Poisson process Geographically Weighted Regression Hydrocephalus, Normal Pressure Java Offset binary Poisson Distribution Poisson regression Population Parameter bismuth subsalicylate 35 MG/ML Oral Suspension micropexophagy-specific membrane apparatus significance level
Content Type	Text
Resource Type	Article

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