MonographDOI
Negative binomial regression, 2nd ed.
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The article was published on 2011-01-01. It has received 1461 citations till now. The article focuses on the topics: Negative multinomial distribution & Binomial proportion confidence interval.read more
Citations
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Journal ArticleDOI
The global distribution and burden of dengue
Samir Bhatt,Peter W. Gething,Oliver J. Brady,Jane P. Messina,Andrew Farlow,Catherine L. Moyes,John M. Drake,John M. Drake,John S. Brownstein,Anne G. Hoen,Osman Sankoh,Osman Sankoh,Monica F. Myers,Dylan B. George,Thomas Jaenisch,G. R. William Wint,Cameron P. Simmons,Thomas W. Scott,Thomas W. Scott,Jeremy Farrar,Jeremy Farrar,Simon I. Hay,Simon I. Hay +22 more
TL;DR: These new risk maps and infection estimates provide novel insights into the global, regional and national public health burden imposed by dengue and will help to guide improvements in disease control strategies using vaccine, drug and vector control methods, and in their economic evaluation.
Journal ArticleDOI
Genome sequence-based species delimitation with confidence intervals and improved distance functions
TL;DR: Despite the high accuracy of GBDP-based DDH prediction, inferences from limited empirical data are always associated with a certain degree of uncertainty, so it is crucial to enrich in-silico DDH replacements with confidence-interval estimation, enabling the user to statistically evaluate the outcomes.
Journal ArticleDOI
A brief introduction to mixed effects modelling and multi-model inference in ecology.
Xavier A. Harrison,Lynda Donaldson,Lynda Donaldson,Maria Eugenia Correa-Cano,Julian C. Evans,Julian C. Evans,David N. Fisher,David N. Fisher,Cecily E. D. Goodwin,Beth S. Robinson,David J. Hodgson,Richard Inger +11 more
TL;DR: This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.
Journal ArticleDOI
Using observation-level random effects to model overdispersion in count data in ecology and evolution
TL;DR: Simulations show that in cases where overdispersion is caused by random extra-Poisson noise, or aggregation in the count data, observation-level random effects yield more accurate parameter estimates compared to when overdisPersion is simply ignored, and that their ability to minimise bias is not uniform across all types of over Dispersion and must be applied judiciously.
Journal ArticleDOI
Early Social-Emotional Functioning and Public Health: The Relationship Between Kindergarten Social Competence and Future Wellness
TL;DR: A kindergarten measure of social-emotional skills may be useful for assessing whether children are at risk for deficits in noncognitive skills later in life and, thus, help identify those in need of early intervention, and demonstrate the relevance of nonc cognitive skills in development for personal and public health outcomes.
References
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Journal ArticleDOI
A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
TL;DR: In this article, a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic is presented, which does not depend on a formal model of the structure of the heteroSkewedness.
Journal ArticleDOI
Sample Selection Bias as a Specification Error
TL;DR: In this article, the bias that results from using non-randomly selected samples to estimate behavioral relationships as an ordinary specification error or "omitted variables" bias is discussed, and the asymptotic distribution of the estimator is derived.
Journal ArticleDOI
Longitudinal data analysis using generalized linear models
Kung Yee Liang,Scott L. Zeger +1 more
TL;DR: In this article, an extension of generalized linear models to the analysis of longitudinal data is proposed, which gives consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence.
Journal ArticleDOI
Generalized Linear Models
TL;DR: In this paper, the authors used iterative weighted linear regression to obtain maximum likelihood estimates of the parameters with observations distributed according to some exponential family and systematic effects that can be made linear by a suitable transformation.