Results 11 to 20 of about 97,649 (287)
POISSON REGRESSION MODELS TO ANALYZE FACTORS THAT INFLUENCE THE NUMBER OF TUBERCULOSIS CASES IN JAVA
Barekeng, 2023 Tuberculosis is an infectious disease and one of the world's top 10 highest causes of mortality in Indonesia. Based on this fact, it is necessary to study what factors affect number of tuberculosis cases.
Yekti Widyaningsih +1 more
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Modelling children ever born using performance evaluation metrics: A dataset
Data in Brief, 2021 Predicting the number of total children ever born in a country is a key component for proper implementation of economic growth policy. Here, performance metrics were used to predict models that appropriately describe the factors that affect children ever
Jecinta U. Ibeji +3 more
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POISSON REGRESSION MODELING GENERALIZED IN MATERNAL MORTALITY CASES IN ACEH TAMIANG REGENCY
Barekeng, 2023 Maternal Mortality Rate (MMR) is the number of maternal deaths due to the process of pregnancy, childbirth, and postpartum which is used as an indicator of women's health degrees.
Riska Novita Sari +2 more
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Journal of New Theory, 2021 The Poisson regression model is widely used for count data. This model assumes equidispersion. In practice, equidispersion is seldom reflected in data. However, in real-life data, the variance usually exceeds the mean.
Burcu Durmuş, Öznur İşçi Güneri
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The Applications of Generalized Poisson Regression Models to Insurance Claim Data
Risks, 2023 Predictive modeling has been widely used for insurance rate making. In this paper, we focus on insurance claim count data and address their common issues with more flexible modeling techniques.
Pouya Faroughi, Shu Li, Jiandong Ren
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Multivariate mixed Poisson Generalized Inverse Gaussian INAR(1) regression
Computational Statistics, 2022 AbstractIn this paper, we present a novel family of multivariate mixed Poisson-Generalized Inverse Gaussian INAR(1), MMPGIG-INAR(1), regression models for modelling time series of overdispersed count response variables in a versatile manner. The statistical properties associated with the proposed family of models are discussed and we derive the joint ...
Chen, Zezhun +2 more
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Regularization for Generalized Additive Mixed Models by Likelihood-Based Boosting [PDF]
, 2011 With the emergence of semi- and nonparametric regression the generalized linear mixed model has been expanded to account for additive predictors. In the present paper an approach to variable selection is proposed that works for generalized additive mixed
Groll, Andreas, Tutz, Gerhard
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Modeling Underdispersed Count Data with Generalized Poisson Regression [PDF]
The Stata Journal: Promoting communications on statistics and Stata, 2012 We present motivation and new Stata commands for modeling count data. While the focus of this article is on modeling data with underdispersion, the new command for fitting generalized Poisson regression models is also suitable as an alternative to negative binomial regression for overdispersed data.
Tammy Harris, Zhao Yang, James W. Hardin
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E-Jurnal Matematika, 2013 The Poisson regression is generally used to analyze the response variable that is a discrete data. Poisson regression has assumption which must be met, that is condition equidispersion.
I PUTU YUDANTA EKA PUTRA +2 more
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Mathematics, 2020 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
Sarni Maniar Berliana +3 more
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