A modified poisson regression approach to prospective studies with binary data.
American Journal of Epidemiology, 2004 Relative risk is usually the parameter of interest in epidemiologic and medical studies. In this paper, the author proposes a modified Poisson regression approach (i.e., Poisson regression with a robust error variance) to estimate this effect measure ...
Guangyong Zou
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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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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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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.
Czado, Claudia, Min, Aleksey
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Modelling Generalized Poisson Regression in the Number of Dengue Hemorrhagic Fever (DHF) in East Nusa Tenggara [PDF]
E3S Web of Conferences, 2020 Regression analysis is an analysis used to model the relationship between the dependent variable (Y) and the independent variable (X). If the dependent variable is a discrete random variable, it is developed using the Poisson regression model.
Prahutama Alan +2 more
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Unweighted regression models perform better than weighted regression techniques for respondent-driven sampling data: results from a simulation study [PDF]
, 2019 Background: It is unclear whether weighted or unweighted regression is preferred in the analysis of data derived from respondent driven sampling. Our objective was to evaluate the validity of various regression models, with and without weights and with ...
Avery, Lisa +5 more
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Forecasting Daily Wildfire Activity Using Poisson Regression
IEEE Transactions on Geoscience and Remote Sensing, 2020 Wildfires and their emissions reduce air quality in many regions of the world, contributing to thousands of premature deaths each year. Smoke forecasting systems have the potential to improve health outcomes by providing future estimates of surface ...
Casey A. Graff +5 more
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On Discrete Poisson-Mirra Distribution: Regression, INAR(1) Process and Applications
Axioms, 2022 Several pieces of research have spotlighted the importance of count data modelling and its applications in real-world phenomena. In light of this, a novel two-parameter compound-Poisson distribution is developed in this paper.
R. Maya +4 more
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Chapter 20: What do interviewers learn? Changes in interview length and interviewer behaviors over the field period. Appendix 20 [PDF]
, 2020 Appendix 20A Full Model Coefficients and Standard Errors Predicting Count of Questions with Individual Interviewer Behaviors, Two-level Multilevel Poisson Models with Number of Questions Asked as Exposure Variable, WLT1 and WLT2 Analytic strategyTable ...
Olson, Kristen M., Smyth, Jolene
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Comparison of Poisson and Quasi-Poisson Regression: A Simulation study
ICSA - International Conference on Statistics and Analytics 2019, 2021 Poisson regression is often used to model count data. However, it requires the assumption of equidispersion which not always met in the real application data. Quasi-Poisson can be considered as an alternative to handle this problem. The objective of this essay is to explain about the Quasi-Poisson regression, the likelihood construction, parameter ...
A Gabriella, S Abdullah, S M Soemartojo
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