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A New Poisson Generalized Lindley Regression Model
Austrian Journal of Statistics, 2023 In this paper, a new count distribution is introduced. It is a mixture of the Poisson and generalized Lindley distributions. Statistical properties of the proposed distribution including the factorial moments, probability generating function, moment ...
Yupapin Atikankul
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OVERDISPERSION HANDLING IN POISSON REGRESSION MODEL BY APPLYING NEGATIVE BINOMIAL REGRESSION
Barekeng, 2023 Statistical analysis that can be used if the response variable is quantified data is Poisson regression, assuming that the assumption must be met equidispersion, where the average response variable is the same as the standard deviation value.
Yesan Tiara +3 more
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Bayesian regression of piecewise homogeneous Poisson processes [PDF]
Papers in Physics, 2015 In this paper, a Bayesian method for piecewise regression is adapted to handle counting processes data distributed as Poisson. A numerical code in Mathematica is developed and tested analyzing simulated data.
Diego Sevilla
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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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A linearization for stable and fast geographically weighted Poisson regression [PDF]
, 2023 Although geographically weighted Poisson regression (GWPR) is a popular regression for spatially indexed count data, its development is relatively limited compared to that found for linear geographically weighted regression (GWR), where many extensions ...
Nakaya, T. +3 more
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Bivariate Poisson and Diagonal Inflated Bivariate Poisson Regression Models in R
Journal of Statistical Software, 2005 In this paper we present an R package called bivpois for maximum likelihood estimation of the parameters of bivariate and diagonal inflated bivariate Poisson regression models. An Expectation-Maximization (EM) algorithm is implemented.
Ioannis Ntzoufras, Dimitris Karlis
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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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Consistency and asymptotic normality of the maximum likelihood estimator in a zero-inflated generalized Poisson regression [PDF]
, 2005 Poisson regression models for count variables have been utilized in many applications. However, in many problems overdispersion and zero-inflation occur.
Min, Aleksey, Czado, Claudia
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POISSON REGRESSION MODELLING OF AUTOMOBILE INSURANCE USING R
Barekeng, 2022 Automobile insurance benefits are protecting the vehicle and minimizing customer losses. Insurance companies must provide funds to pay customer claims if a claim occurs. Insurance claims can be modelled by Poisson regression.
Sandy Vantika +2 more
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E-Jurnal Matematika, 2014 Poisson regression is a nonlinear regression that is often used to model count response variable and categorical, interval, or count regressor. This regression assumes equidispersion, i.e., the variance equals the mean.
NI MADE RARA KESWARI +2 more
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