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Score tests for zero-inflated double poisson regression models
Acta Mathematicae Applicatae Sinica, English Series, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xie, Feng-Chang +2 more
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A score test for overdispersion in zero‐inflated poisson mixed regression model
Statistics in Medicine, 2006AbstractCount data with extra zeros are common in many medical applications. The zero‐inflated Poisson (ZIP) regression model is useful to analyse such data. For hierarchical or correlated count data where the observations are either clustered or represent repeated outcomes from individual subjects, a class of ZIP mixed regression models may be ...
Xiang, L. +3 more
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Two-step testing in employee selection: is score inflation a problem?
Human Resource Management International Digest, 2008Unproctored Internet testing in employee selection has become increasingly popular over the past few years. However, there is a concern that cheating during unproctored administrations may influence the test results in terms of score inflation. The current research attempts to determine the extent of cheating on an unproctored Internet test of ...
Christopher D. Nye +3 more
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Weighted Score test based EWMA control charts for Zero-Inflated Poisson Models
Computers & Industrial Engineering, 2021Abstract Zero-inflated Poisson models have been widely used to account for excess zero values in count data. Recent literature pointed out that the quality characteristic could be assumed to depend on a linear function of covariates in zero-inflated Poisson models, which is called risk-adjustment.
Qiuyan Hu, Liu Liu 0002
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A Score Test for Zero Inflation in a Poisson Distribution
Biometrics, 1995When analyzing Poisson-count data sometimes a lot of zeros are observed. When there are too many zeros a zero-inflated Poisson distribution can be used. A score test is presented to test whether the number of zeros is too large for a Poisson distribution to fit the data well.
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Journal of Applied Statistics, 2013
In several cases, count data often have excessive number of zero outcomes. This zero-inflated phenomenon is a specific cause of overdispersion, and zero-inflated Poisson regression model (ZIP) has been proposed for accommodating zero-inflated data. However, if the data continue to suggest additional overdispersion, zero-inflated negative binomial (ZINB)
Hossein Zamani, Noriszura Ismail
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In several cases, count data often have excessive number of zero outcomes. This zero-inflated phenomenon is a specific cause of overdispersion, and zero-inflated Poisson regression model (ZIP) has been proposed for accommodating zero-inflated data. However, if the data continue to suggest additional overdispersion, zero-inflated negative binomial (ZINB)
Hossein Zamani, Noriszura Ismail
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Score tests for zero-inflated generalized Poisson mixed regression models
Computational Statistics & Data Analysis, 2009zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Feng-Chang Xie +2 more
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Score Test for Zero Inflated Generalized Poisson Regression Model
Communications in Statistics - Theory and Methods, 2005In certain applications involving count data, it is sometimes found that zeros are observed with a frequency significantly higher (lower) than predicted by the assumed model. Examples of such applications are cited in the literature from engineering, manufacturing, economics, public health, epidemiology, psychology, sociology, political science ...
Pushpa Lata Gupta +2 more
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Score Tests for Extra-Zero Models in Zero-Inflated Negative Binomial Models
Communications in Statistics - Simulation and Computation, 2008When overdispersion is present in count data, a negative binomial (NB) model is commonly used in place of the standard Poisson model. However, the model is sometimes not adequate because of the occurrence of excess zeros and a zero-inflated negative binomial (ZNB) model may be more appropriate.
N. Jansakul, John P. Hinde
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Canadian Journal of Statistics, 2002
AbstractHall (2000) has described zero‐inflated Poisson and binomial regression models that include random effects to account for excess zeros and additional sources of heterogeneity in the data. The authors of the present paper propose a general score test for the null hypothesis that variance components associated with these random effects are zero ...
Hall, Daniel B., Berenhaut, Kenneth S.
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AbstractHall (2000) has described zero‐inflated Poisson and binomial regression models that include random effects to account for excess zeros and additional sources of heterogeneity in the data. The authors of the present paper propose a general score test for the null hypothesis that variance components associated with these random effects are zero ...
Hall, Daniel B., Berenhaut, Kenneth S.
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