Open AccessBook
Negative Binomial Regression
TLDR
In this article, the authors introduce the concept of risk in count response models and assess the performance of count models, including Poisson regression, negative binomial regression, and truncated count models.Abstract:
Preface 1. Introduction 2. The concept of risk 3. Overview of count response models 4. Methods of estimation and assessment 5. Assessment of count models 6. Poisson regression 7. Overdispersion 8. Negative binomial regression 9. Negative binomial regression: modeling 10. Alternative variance parameterizations 11. Problems with zero counts 12. Censored and truncated count models 13. Handling endogeneity and latent class models 14. Count panel models 15. Bayesian negative binomial models Appendix A. Constructing and interpreting interactions Appendix B. Data sets and Stata files References Index.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.
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A protocol for data exploration to avoid common statistical problems
TL;DR: A protocol for data exploration is provided; current tools to detect outliers, heterogeneity of variance, collinearity, dependence of observations, problems with interactions, double zeros in multivariate analysis, zero inflation in generalized linear modelling, and the correct type of relationships between dependent and independent variables are discussed; and advice on how to address these problems when they arise is provided.
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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
Social contacts and mixing patterns relevant to the spread of infectious diseases.
Joël Mossong,Niel Hens,Mark Jit,Philippe Beutels,Kari Auranen,Rafael T. Mikolajczyk,Marco Massari,Stefania Salmaso,Gianpaolo Scalia Tomba,Jacco Wallinga,Janneke C. M. Heijne,Malgorzata Sadkowska-Todys,Magdalena Rosinska,W. John Edmunds +13 more
TL;DR: This study provides the first large-scale quantitative approach to contact patterns relevant for infections transmitted by the respiratory or close-contact route, and the results should lead to improved parameterisation of mathematical models used to design control strategies.
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.
References
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Estimating the dimension of a model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Book
Applied Logistic Regression
David W. Hosmer,Stanley Lemeshow +1 more
TL;DR: Hosmer and Lemeshow as discussed by the authors provide an accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets.
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.