Results 301 to 310 of about 24,139,341 (352)
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Journal of the American Statistical Association, 2006
In this article we characterize all two-parameter count distributions (satisfying very general conditions) that are partially closed under addition. We also find those for which the maximum likelihood estimator of the population mean is the sample mean. Mixed Poisson models satisfying these properties are completely determined.
Puig, Pedro, Valero, Jordi
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In this article we characterize all two-parameter count distributions (satisfying very general conditions) that are partially closed under addition. We also find those for which the maximum likelihood estimator of the population mean is the sample mean. Mixed Poisson models satisfying these properties are completely determined.
Puig, Pedro, Valero, Jordi
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Testing for Trend with Count Data
Biometrics, 1998Among the tests that can be used to detect dose-related trends in count data from toxicological studies are nonparametric tests such as the Jonckheere-Terpstra and likelihood-based tests, for example, based on a Poisson model. This paper was motivated by a data set of tumor counts in which conflicting conclusions were obtained using these two tests. To
Weller, Edie A., Ryan, Louise M.
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Simulating correlated count data
Environmental and Ecological Statistics, 2007In this study we compare two techniques for simulating count-valued random n-vectors Y with specified mean and correlation structure. The first technique is to use a lognormal-Poisson hierarchy (L-P method). A vector of correlated normals Z is generated and transformed to a vector of lognormals X.
L. Madsen, D. Dalthorp
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Modeling Longitudinal Count Data
Sociological Methods & Research, 2012To test for group differences in growth trajectories in mixed (fixed and random effects) models, researchers frequently interpret the coefficient of Group-by-Time product terms. While this practice is straightforward in linear mixed models, it is less so in generalized linear mixed models.
Sarah Mustillo +2 more
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Data Metrics and Make Data Count
2023An update on data metrics and the Make Data Count project at DataCite Open Hours on 6 December 2023.
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2000
Multivariate count data are likely to have a non-trivial correlation structure. For instance, omitted variables may simultaneously affect more than one count. The modeling of the correlation structure is important for the efficiency of the estimator and the computation of correct standard errors, i.e., valid inference.
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Multivariate count data are likely to have a non-trivial correlation structure. For instance, omitted variables may simultaneously affect more than one count. The modeling of the correlation structure is important for the efficiency of the estimator and the computation of correct standard errors, i.e., valid inference.
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OD Count Estimation Based on Link Count Data
2008TM (Traffic Matrix) estimation is a hot research area recently. Current TM estimation methods are generally designed for backbone and ISP networks. They estimate complete TM which is unnecessary for many IP networks in reality and especially unsuitable for the networks that have many entries.
Yi Jin +5 more
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2014
This entry-level text offers clear and concise guidelines on how to select, construct, interpret, and evaluate count data. Written for researchers with little or no background in advanced statistics, the book presents treatments of all major models using numerous tables, insets, and detailed modeling suggestions.
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This entry-level text offers clear and concise guidelines on how to select, construct, interpret, and evaluate count data. Written for researchers with little or no background in advanced statistics, the book presents treatments of all major models using numerous tables, insets, and detailed modeling suggestions.
openaire +1 more source