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Overdispersion and Poisson Regression
Journal of Quantitative Criminology, 2008This article discusses the use of regression models for count data. A claim is often made in criminology applications that the negative binomial distribution is the conditional distribution of choice when for a count response variable there is evidence of overdispersion.
John M. MacDonald, Richard A. Berk
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Poisson Regression with Missing Durations of Exposure [PDF]
Biometrics, 1999 Summary. In this paper, we develop Poisson‐type regression methods that require the durations of exposure be measured only on a possibly nonrandom subset of the cohort members. These methods can be used to make inferences about the incidence density during exposure as well as the ratio of incidence densities during exposure versus not during exposure.
Louise M. Ryan +4 more
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1992
It is well-known among applied researchers that the assumption of equality of conditional mean and variance in the Poisson model is rather restrictive, especially that there is a tendency for too low standard errors in case of overdispersion. Pre-tests or more general models have been proposed to solve the problem.
Rainer Winkelmann, Klaus F. Zimmermann
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It is well-known among applied researchers that the assumption of equality of conditional mean and variance in the Poisson model is rather restrictive, especially that there is a tendency for too low standard errors in case of overdispersion. Pre-tests or more general models have been proposed to solve the problem.
Rainer Winkelmann, Klaus F. Zimmermann
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POISSON REGRESSION WITH A PERIODIC FUNCTION [PDF]
Communications in Statistics - Theory and Methods, 2002 ABSTRACT Let {yt } be a Poisson-like process with the mean μ t which is a periodic function of time t. We discuss how to fit this type of data set using quasi-likelihood method. Our method provides a new avenue to fit a time series data when the usual assumption of stationarity and homogeneous residual variances are invalid. We show that the estimators
Debasis Kundu, Naveen K. Bansal
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Sensitivity of Test for Overdispersion in Poisson Regression
Biometrical Journal, 2005Overdispersion or extra-Poisson variation is very common for count data. This phenomenon arises when the variability of the counts greatly exceeds the mean under the Poisson assumption, resulting in substantial bias for the parameter estimates. To detect whether count data are overdispersed in the Poisson regression setting, various tests have been ...
Andy H. Lee, Liming Xiang, Liming Xiang
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Journal of Statistical Planning and Inference, 2006
Abstract Count data are very often analyzed under the assumption of a Poisson model [( Agresti, A., 1996 . An Introduction to Categorical Data Analysis. Wiley, New York; Generalized Linear Models, second ed. Chapman & Hall, New York)]. However, the derived inference is generally erroneous if the underlying distribution is not Poisson (Biometrika 70 ...
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Abstract Count data are very often analyzed under the assumption of a Poisson model [( Agresti, A., 1996 . An Introduction to Categorical Data Analysis. Wiley, New York; Generalized Linear Models, second ed. Chapman & Hall, New York)]. However, the derived inference is generally erroneous if the underlying distribution is not Poisson (Biometrika 70 ...
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Silpakorn University Science and Technology Journal, 2016
Silpakorn University Science and Technology Journal, 10 ...
Maysiya Yamjaroenkit +2 more
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Silpakorn University Science and Technology Journal, 10 ...
Maysiya Yamjaroenkit +2 more
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Improved two-parameter estimators for the negative binomial and Poisson regression models
Journal of Statistical Computation and Simulation, 2019Negative binomial regression (NBR) and Poisson regression (PR) applications have become very popular in the analysis of count data in recent years. However, if there is a high degree of relationship between the independent variables, the problem of ...
Merve Kandemir Çetinkaya +1 more
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Sample Size for Poisson Regression
Biometrika, 1991SUMMARY For the Poisson regression model, an exact expression for Fisher's information matrix, based upon the moment generating function of the distribution of covariates, is calculated. This parallels a similar, approximate, calculation by Whittemore (1981) for logistic regression.
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Regression correlation coefficient for a Poisson regression model
Computational Statistics & Data Analysis, 2016This study examines measures of predictive power for a generalized linear model (GLM). Although many measures of predictive power for GLMs have been proposed, most have limitations. Hence, we focus on the regression correlation coefficient (RCC) (Zheng and Agresti, 2000), which satisfies the four requirements of (i) interpretability, (ii) applicability,
Takeshi Kurosawa, Akihito Takahashi
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