compois.bib

@article{Bates2015,
abstract = {lme4: Mixed-effects modeling with R},
author = {Bates, Douglas M and Maechler, Martin and Bolker, Ben and Walker, Steve},
doi = {10.1177/009286150103500418},
file = {:home/eduardo/Documents/Mendeley Desktop/lrgprt.pdf:pdf},
issn = {0092-8615},
journal = {Journal of Statistical Software},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
pages = {1--48},
title = {{Fitting linear mixed-effects models using lme4}},
url = {http://lme4.r-forge.r-project.org/lMMwR/lrgprt.pdf},
volume = {67},
year = {2015}
}
@phdthesis{Borges2012,
author = {Borges, Patrick},
file = {:home/eduardo/Documents/Mendeley Desktop/4552.pdf:pdf},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
school = {Universidade Federal de S{\~{a}}o Carlos},
title = {{Novos modelos de sobreviv{\^{e}}ncia com fra{\c{c}}{\~{a}}o de cura baseados no processo da carcinog{\^{e}}nese}},
year = {2012}
}
@article{Conway1962,
author = {Conway, Richard W and Maxwell, William L},
journal = {Journal of Industrial Engineering},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
pages = {132----136},
title = {{A queuing model with state dependent service rates}},
volume = {12},
year = {1962}
}
@book{Ferreira2011,
author = {Ferreira, Daniel Furtado},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
publisher = {Editora UFLA},
title = {{Estat{\'{i}}stica Multivariada}},
year = {2011}
}
@book{Hilbe2014,
abstract = {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. It begins by demonstrating the fundamentals of linear regression and works up to an analysis of the Poisson and negative binomial models, and to the problem of overdispersion. Examples in Stata, R, and SAS code enable readers to adapt models for their own purposes, making the text an ideal resource for researchers working in public health, ecology, econometrics, transportation, and other related fields.},
author = {Hilbe, Joseph M.},
booktitle = {Statistical Science},
doi = {10.1017/CBO9781139236065},
file = {:home/eduardo/Documents/Mendeley Desktop/Hilbe - 2014 - Modeling Count Data.pdf:pdf},
isbn = {ISBN 978-1-107-02833-3},
issn = {1467-9280},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
pages = {300},
pmid = {25052830},
title = {{Modeling Count Data}},
volume = {25},
year = {2014}
}
@article{King1989,
abstract = {This paper discusses the problem of variance specification in models for event count data. Event counts are dependent variables that can take on only nonnegative integer values, such as the number of wars or coups d'etat in a year. I discuss several generalizations of the Poisson regression model, presented in King (1988), to allow for substantively interesting stochastic processes that do not fit into the Poisson framework. Individual models that cope with, and help analyze, heterogeneity, contagion, and negative contagion are each shown to lead to specific statistical models for event count data. In addition, I derive a new generalized event count (GEC) model that enables researchers to extract significant amounts of new information from existing data by estimating features of these unobserved substantive processes. Applications of this model to congressional challenges of presidential vetoes and superpower conflict demonstrate the dramatic advantages of this approach.},
author = {King, Gary},
doi = {10.2307/2111071},
file = {:home/eduardo/Documents/Mendeley Desktop/King - 1989 - Variance Specification in Event Count Models From Restrictive Assumptions to a Generalized Estimator.pdf:pdf},
isbn = {00925853},
issn = {00925853},
journal = {American Journal of Political Science},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
month = {aug},
number = {3},
pages = {762--784},
title = {{Variance specification in event count models: from restrictive assumptions to a generalized estimator}},
url = {http://www.jstor.org/stable/2111071},
volume = {33},
year = {1989}
}
@incollection{Kokonendji2014,
author = {Kokonendji, Celestin C.},
booktitle = {Methods and Applications of Statistics in Clinical Trials: Planning, Analysis, and Inferential Methods},
file = {:home/eduardo/Documents/Mendeley Desktop/ch30{\_}kokonendji2014.pdf:pdf},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
pages = {506--526},
title = {{Over- and Underdisperson Models}},
url = {https://lmb.univ-fcomte.fr/IMG/pdf/ch30{\_}kokonendji2014.pdf},
year = {2014}
}
@article{Lambert1992,
author = {Lambert, Diane},
doi = {10.2307/1269547},
file = {:home/eduardo/Documents/Mendeley Desktop/lambert1992.pdf:pdf},
issn = {00401706},
journal = {Technometrics},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
month = {feb},
number = {1},
pages = {1},
title = {{Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing}},
url = {http://www.jstor.org/stable/1269547?origin=crossref},
volume = {34},
year = {1992}
}
@article{Lord2010,
abstract = {The objective of this article is to evaluate the performance of the COM-Poisson GLM for analyzing crash data exhibiting underdispersion (when conditional on the mean). The COM-Poisson distribution, originally developed in 1962, has recently been reintroduced by statisticians for analyzing count data subjected to either over- or underdispersion. Over the last year, the COM-Poisson GLM has been evaluated in the context of crash data analysis and it has been shown that the model performs as well as the Poisson-gamma model for crash data exhibiting overdispersion. To accomplish the objective of this study, several COM-Poisson models were estimated using crash data collected at 162 railway-highway crossings in South Korea between 1998 and 2002. This data set has been shown to exhibit underdispersion when models linking crash data to various explanatory variables are estimated. The modeling results were compared to those produced from the Poisson and gamma probability models documented in a previous published study. The results of this research show that the COM-Poisson GLM can handle crash data when the modeling output shows signs of underdispersion. Finally, they also show that the model proposed in this study provides better statistical performance than the gamma probability and the traditional Poisson models, at least for this data set.},
author = {Lord, Dominique and Geedipally, Srinivas Reddy and Guikema, Seth D.},
doi = {10.1111/j.1539-6924.2010.01417.x},
file = {:home/eduardo/Documents/Mendeley Desktop/Lord, Geedipally, Guikema - 2010 - Extension of the application of conway-maxwell-poisson models Analyzing traffic crash data exhibiting.pdf:pdf},
isbn = {1539-6924 (Electronic) 0272-4332 (Linking)},
issn = {02724332},
journal = {Risk Analysis},
keywords = {Com-poisson,Conway-Maxwell-Poisson,gamma models,negative binomial models,regression models,underdispersion},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
mendeley-tags = {Com-poisson},
number = {8},
pages = {1268--1276},
pmid = {20412518},
title = {{Extension of the application of conway-maxwell-poisson models: Analyzing traffic crash data exhibiting underdispersion}},
volume = {30},
year = {2010}
}
@article{Nelder1972,
author = {Nelder, John Ashworth and Wedderburn, Robert William Maclagan},
file = {:home/eduardo/Documents/Mendeley Desktop/Nelder, Wedderburn - 1972 - Generalized Linear Models.pdf:pdf},
journal = {Journal of the Royal Statistical Society. Series A (General)},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
pages = {370--384},
title = {{Generalized Linear Models}},
volume = {135},
year = {1972}
}
@book{Nocedal1995,
abstract = {This is a book for people interested in solving optimization problems. Because of the wide (and growing) use of optimization in science, engineering, economics, and industry, it is essential for students and practitioners alike to develop an understanding of optimization algorithms. Knowledge of the capabilities and limitations of these algorithms leads to a better understanding of their impact on various applications, and points the way to future research on improving and extending optimization algorithms and software. Our goal in this book is to give a comprehensive description of the most powerful, state-of-the-art, techniques for solving continuous optimization problems. By presenting the motivating ideas for each algorithm, we try to stimulate the reader's intuition and make the technical details easier to follow. Formal mathematical requirements are kept to a minimum. Because of our focus on continuous problems, we have omitted discussion of important optimization topics such as discrete and stochastic optimization.},
author = {Nocedal, Jorge and Wright, Stephen J.},
booktitle = {International ADAMS user conference},
doi = {10.1007/BF01068601},
file = {:home/eduardo/Documents/Mendeley Desktop/Numerical{\_}Optimization.pdf:pdf},
isbn = {0387987932},
issn = {0011-4235},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
pages = {636},
pmid = {21384397},
publisher = {Springer},
title = {{Numerical optimization}},
url = {https://books.google.com/books?id=epc5fX0lqRIC{\&}pgis=1},
year = {1995}
}
@article{Park2009,
abstract = {Developing sound or reliable statistical models for analyzing motor vehicle crashes is very important in highway safety studies. However, a significant difficulty associated with the model development is related to the fact that crash data often exhibit over-dispersion. Sources of dispersion can be varied and are usually unknown to the transportation analysts. These sources could potentially affect the development of negative binomial (NB) regression models, which are often the model of choice in highway safety. To help in this endeavor, this paper documents an alternative formulation that could be used for capturing heterogeneity in crash count models through the use of finite mixture regression models. The finite mixtures of Poisson or NB regression models are especially useful where count data were drawn from heterogeneous populations. These models can help determine sub-populations or groups in the data among others. To evaluate these models, Poisson and NB mixture models were estimated using data collected in Toronto, Ontario. These models were compared to standard NB regression model estimated using the same data. The results of this study show that the dataset seemed to be generated from two distinct sub-populations, each having different regression coefficients and degrees of over-dispersion. Although over-dispersion in crash data can be dealt with in a variety of ways, the mixture model can help provide the nature of the over-dispersion in the data. It is therefore recommended that transportation safety analysts use this type of model before the traditional NB model, especially when the data are suspected to belong to different groups.},
author = {Park, Byung-Jung and Lord, Dominique},
doi = {10.1016/j.aap.2009.03.007},
file = {:home/eduardo/Documents/Mendeley Desktop/Park, Lord - 2009 - Application of finite mixture models for vehicle crash data analysis.pdf:pdf;:home/eduardo/Documents/Mendeley Desktop/Park, Lord - 2009 - Application of finite mixture models for vehicle crash data analysis(2).pdf:pdf},
issn = {1879-2057},
journal = {Accident; analysis and prevention},
keywords = {Com-poisson},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
mendeley-tags = {Com-poisson},
number = {4},
pages = {683--691},
pmid = {19540956},
title = {{Application of finite mixture models for vehicle crash data analysis.}},
volume = {41},
year = {2009}
}
@book{Paula2013,
abstract = {A {\'{a}}rea de modelagem estat{\'{i}}stica de regress{\~{a}}o recebeu um grande impulso desde a cria{\c{c}}{\~{a}}o dos modelos lineares generalizados (MLGs) no in{\'{i}}cio da d{\'{e}}- cada de 70. O crescente interesse pela {\'{a}}rea motivou a realiza{\c{c}}{\~{a}}o de v{\'{a}}rios encontros informais no in{\'{i}}cio dos anos 80, a maioria deles na Inglaterra, at{\'{e}} que em 1986 foi realizado na cidade de Innsbruck na {\'{A}}ustria o “1st Internati- onalWorkshop on Statistical Modelling”(1st IWSM). Esse encontro tem sido realizado anualmente sendo que o {\'{u}}ltimo (25th IWSM) aconteceu em julho de 2010 na Universidade de Glasgow, Esc{\'{o}}cia. O 26th IWSM ser{\'{a}} realizado em julho de 2011 em Val{\^{e}}ncia, Espanha. No Brasil a {\'{a}}rea come{\c{c}}ou efetiva- mente a se desenvolver a partir de meados da d{\'{e}}cada de 80 e em particular ap{\'{o}}s a 1a Escola de Modelos de Regress{\~{a}}o (1EMR) realizada na Universi- dade de S{\~{a}}o Paulo em 1989. As demais escolas ocorreram desde ent{\~{a}}o a cada dois anos sendo que a {\'{u}}ltima (11EMR) foi realizada em mar{\c{c}}o de 2009 na cidade de Recife, PE. A 12EMR ser{\'{a}} realizada em mar{\c{c}}o de 2011 na cidade de Fortaleza, CE.},
author = {Paula, Gilberto Alvarenga},
file = {:home/eduardo/Documents/Mendeley Desktop/Paula - 2013 - Modelos de regress{\~{a}}o com apoio computacional.pdf:pdf},
keywords = {GLM,Regress{\~{a}}o},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
mendeley-tags = {GLM,Regress{\~{a}}o},
publisher = {IME-USP S{\~{a}}o Paulo},
title = {{Modelos de regress{\~{a}}o com apoio computacional}},
url = {https://www.ime.usp.br/{~}giapaula/textoregressao.htm},
year = {2013}
}
@inproceedings{RibeiroJr2012,
author = {{Ribeiro Jr}, Paulo Justiniano and Bonat, Wagner Hugo and Krainski, Elias Teixeira and Zeviani, Walmes Marques},
booktitle = {20{\textordmasculine} Simp{\'{o}}sio Nacional de Probabilidade e Estat{\'{i}}stica},
file = {:home/eduardo/Documents/Mendeley Desktop/Ribeiro Jr et al. - 2012 - M{\'{e}}todos computacionais para infer{\^{e}}ncia com aplica{\c{c}}{\~{o}}es em R.pdf:pdf},
keywords = {Infer{\^{e}}ncia,M{\'{e}}todos Computacionais,Verossimilhan{\c{c}}a},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
mendeley-tags = {Infer{\^{e}}ncia,M{\'{e}}todos Computacionais,Verossimilhan{\c{c}}a},
pages = {282},
title = {{M{\'{e}}todos computacionais para infer{\^{e}}ncia com aplica{\c{c}}{\~{o}}es em R}},
url = {http://leg.ufpr.br/doku.php/cursos:mcie},
year = {2012}
}
@phdthesis{Ribeiro2012,
author = {Ribeiro, Ang{\'{e}}lica Maria Tortola},
file = {:home/eduardo/Documents/Mendeley Desktop/4336.pdf:pdf},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
school = {Universidade Federal de S{\~{a}}o Carlos},
title = {{Distribui{\c{c}}{\~{a}}o COM-Poisson na an{\'{a}}lise de dados de experimentos de quimiopreven{\c{c}}{\~{a}}o do c{\^{a}}ncer em animais}},
year = {2012}
}
@article{Ridout1998,
abstract = {We consider the problem of modelling count data with excess zeros and review some possible models. Aspects of model tting and inference are considered. An example from horticultural research is used for illustration.},
author = {Ridout, Martin and Demetrio, Clarice G.B and Hinde, John},
file = {:home/eduardo/Documents/Mendeley Desktop/ibc{\_}fin.pdf:pdf},
journal = {International Biometric Conference},
keywords = {count data,em algorithm,hurdle models,negative binomial,poisson,zero inflation},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
number = {December},
pages = {1--13},
title = {{Models for count data with many zeros}},
year = {1998}
}
@article{Sellers2016,
abstract = {Excess zeroes are often thought of as a cause of data over-dispersion (i.e. when the variance exceeds the mean); this claim is not entirely accurate. In actuality, excess zeroes reduce the mean of a dataset, thus inflating the dispersion index (i.e. the variance divided by the mean). While this results in an increased chance for data over-dispersion, the implication is not guaranteed. Thus, one should consider a flexible distribution that not only can account for excess zeroes, but can also address potential over- or under-dispersion. A zero-inflated Conway-Maxwell-Poisson (ZICMP) regression allows for modeling the relationship between explanatory and response variables, while capturing the effects due to excess zeroes and dispersion. This work derives the ZICMP model and illustrates its flexibility, extrapolates the corresponding likelihood ratio test for the presence of significant data dispersion, and highlights various statistical properties and model fit through several examples.},
author = {Sellers, Kimberly F. and Raim, Andrew},
doi = {10.1016/j.csda.2016.01.007},
file = {:home/eduardo/Documents/Mendeley Desktop/compoissonzeroinflated.pdf:pdf},
issn = {01679473},
journal = {Computational Statistics {\&} Data Analysis},
keywords = {Conway-Maxwell-Poisson,Excess zeroes,Over-dispersion,Under-dispersion},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
month = {jul},
pages = {68--80},
publisher = {Elsevier B.V.},
title = {{A flexible zero-inflated model to address data dispersion}},
url = {http://dx.doi.org/10.1016/j.csda.2016.01.007 http://linkinghub.elsevier.com/retrieve/pii/S0167947316000165},
volume = {99},
year = {2016}
}
@article{Sellers2010,
abstract = {Poisson regression is a popular tool for modeling count data and is applied in a vast array of applications from the social to the physical sciences and beyond. Real data, however, are often over- or under-dispersed and, thus, not conducive to Poisson regression. We propose a regression model based on the Conway--Maxwell-Poisson (COM-Poisson) distribution to address this problem. The COM-Poisson regression generalizes the well-known Poisson and logistic regression models, and is suitable for fitting count data with a wide range of dispersion levels. With a GLM approach that takes advantage of exponential family properties, we discuss model estimation, inference, diagnostics, and interpretation, and present a test for determining the need for a COM-Poisson regression over a standard Poisson regression. We compare the COM-Poisson to several alternatives and illustrate its advantages and usefulness using three data sets with varying dispersion.},
annote = {Refer{\^{e}}ncia para COMPoissonReg package},
archivePrefix = {arXiv},
arxivId = {1011.2077},
author = {Sellers, Kimberly F. and Shmueli, Galit},
doi = {10.1214/09-AOAS306},
eprint = {1011.2077},
file = {:home/eduardo/Documents/Mendeley Desktop/Sellers, Shmueli - 2010 - A flexible regression model for count data.pdf:pdf;:home/eduardo/Documents/Mendeley Desktop/Sellers, Shmueli - 2010 - A flexible regression model for count data(2).pdf:pdf},
issn = {19326157},
journal = {Annals of Applied Statistics},
keywords = {Com-poisson,Conway-Maxwell-Poisson (COM-Poisson) distribution,Dispersion,Generalized Poisson,Generalized linear models (GLM)},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
mendeley-tags = {Com-poisson},
number = {2},
pages = {943--961},
title = {{A flexible regression model for count data}},
volume = {4},
year = {2010}
}
@article{Serafim2012,
abstract = {Os solos tropicais, normalmente pobres em pot{\'{a}}ssio (K), quando cultivados com soja Glycine max L.) demandam aduba{\c{c}}{\~{a}}o pot{\'{a}}ssica para obten{\c{c}}{\~{a}}o de produtividades satisfat{\'{o}}rias. Objetivou-se com este trabalho avaliar o efeito de doses de K e n{\'{i}}veis de umidade do solo nos componentes de produ{\c{c}}{\~{a}}o da soja. O experimento foi realizado em casa de vegeta{\c{c}}{\~{a}}o e os tratamentos foram dispostos em um delineamento em blocos casualizados em esquema fatorial (5 x 3), com cinco repeti{\c{c}}{\~{o}}es. Um vaso com 5 dm 3 de solo com duas plantas comp{\^{o}}s a parcela experimental. O primeiro fator correspondeu {\`{a}}s doses de K (0; 30; 60; 120 e 180 mg dm -3 e o segundo fator as faixas de umidade do solo (35 a 40; 47,5 a 52,5; e 60 a 65{\%} do volume total de poros). Foram avaliados: rendimento de gr{\~{a}}os, peso de cem gr{\~{a}}os, n{\'{u}}mero total de gr{\~{a}}os por vaso, teor de K no gr{\~{a}}o, n{\'{u}}mero de vagens vi{\'{a}}veis. Houve resposta da soja {\`{a}} aduba{\c{c}}{\~{a}}o pot{\'{a}}ssica, com aumento do rendimento de gr{\~{a}}os, massa de cem gr{\~{a}}os, teor de K no gr{\~{a}}o e n{\'{u}}mero de vagens vi{\'{a}}veis. O total de gr{\~{a}}os por vaso atingiu valor m{\'{a}}ximo na combina{\c{c}}{\~{a}}o dos limites superiores de cada fator de estudo. O K reduziu os efeitos do d{\'{e}}fice h{\'{i}}drico na cultura da soja.},
author = {Serafim, Milson Evaldo and Ono, F{\'{a}}bio Benedito and Zeviani, Walmes Marques and Novelino, Jos{\'{e}} Oscar and Silva, Joil Vilhalva},
doi = {10.1590/S1806-66902012000200003},
file = {:home/eduardo/Documents/Mendeley Desktop/a03v43n2.pdf:pdf},
issn = {1806-6690},
journal = {Revista Ci{\^{e}}ncia Agron{\^{o}}mica},
keywords = {Glycine max,Potassium,Soil-humidity},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
month = {jun},
number = {2},
pages = {222--227},
title = {{Umidade do solo e doses de pot{\'{a}}ssio na cultura da soja}},
url = {http://www.scielo.br/scielo.php?script=sci{\_}arttext{\&}pid=S1806-66902012000200003{\&}lng=pt{\&}nrm=iso{\&}tlng=en},
volume = {43},
year = {2012}
}
@article{Shmueli2005,
abstract = {A useful discrete distribution (the Conway2013Maxwell2013Poisson distribution) is revived and its statistical and probabilistic properties are introduced and explored. This distribution is a two-parameter extension of the Poisson distribution that generalizes some well-known discrete distributions (Poisson, Bernoulli and geometric). It also leads to the generalization of distributions derived from these discrete distributions (i.e. the binomial and negative binomial distributions). We describe three methods for estimating the parameters of the Conway2013Maxwell2013Poisson distribution. The first is a fast simple weighted least squares method, which leads to estimates that are sufficiently accurate for practical purposes. The second method, using maximum likelihood, can be used to refine the initial estimates. This method requires iterations and is more computationally intensive. The third estimation method is Bayesian. Using the conjugate prior, the posterior density of the parameters of the Conway2013Maxwell2013Poisson distribution is easily computed. It is a flexible distribution that can account for overdispersion or underdispersion that is commonly encountered in count data. We also explore two sets of real world data demonstrating the flexibility and elegance of the Conway2013Maxwell2013Poisson distribution in fitting count data which do not seem to follow the Poisson distribution.},
annote = {Refer{\^{e}}ncia para compoisson package},
author = {Shmueli, Galit and Minka, Thomas P. and Kadane, Joseph B. and Borle, Sharad and Boatwright, Peter},
doi = {10.1111/j.1467-9876.2005.00474.x},
file = {:home/eduardo/Documents/Mendeley Desktop/Shmueli et al. - 2005 - A useful distribution for fitting discrete data Revival of the Conway-Maxwell-Poisson distribution.pdf:pdf},
isbn = {1467-9876},
issn = {00359254},
journal = {Journal of the Royal Statistical Society. Series C: Applied Statistics},
keywords = {Com-poisson,Conjugate family,Conway-Maxwell-Poisson distribution,Estimation,Exponential family,Overdispersion,Underdispersion},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
mendeley-tags = {Com-poisson},
number = {1},
pages = {127--142},
title = {{A useful distribution for fitting discrete data: Revival of the Conway-Maxwell-Poisson distribution}},
volume = {54},
year = {2005}
}
@article{Silva2012,
author = {Silva, A. M. and Degrande, P. E. and Suekane, R. and Fernandes, M. G. and Zeviani, W. M.},
file = {:home/eduardo/Documents/Mendeley Desktop/v35n1a16.pdf:pdf},
issn = {0871-018X},
journal = {Revista de Ci{\^{e}}ncias Agr{\'{a}}rias},
keywords = {bolls,cotton,crop growth stage,crop yield,defoliation,leaf area,phenology,productivity,yield losses},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
number = {1},
pages = {163--172},
title = {{Impacto de diferentes n{\'{i}}veis de desfolha artificial nos est{\'{a}}dios fenol{\'{o}}gicos do algodoeiro}},
url = {http://www.cabdirect.org/abstracts/20123299470.html;jsessionid=CF06663390A3A4463413D3018ECAACD6},
volume = {35},
year = {2012}
}
@phdthesis{Suekane2011,
author = {Suekane, Renato},
file = {:home/eduardo/Documents/Mendeley Desktop/Disserta{\c{c}}{\~{a}}o Renato Suekane.pdf:pdf},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
school = {Universidade Federal da Grande Dourados},
title = {{DISTRIBUI{\c{C}}{\~{A}}O ESPACIAL E DANO DE MOSCA-BRANCA Bemisia tabaci (GENNADIUS, 1889) BI{\'{O}}TIPO B NA SOJA}},
year = {2011}
}
@article{Wedderburn1974,
author = {Wedderburn, R. W. M.},
doi = {10.2307/2334725},
file = {:home/eduardo/Documents/Mendeley Desktop/wedderburn1974.pdf:pdf},
issn = {00063444},
journal = {Biometrika},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
month = {dec},
number = {3},
pages = {439},
title = {{Quasi-Likelihood Functions, Generalized Linear Models, and the Gauss-Newton Method}},
url = {http://www.jstor.org/stable/2334725?origin=crossref},
volume = {61},
year = {1974}
}
@misc{Winkelmann1994,
abstract = {"This paper deals with the estimation of single equation models in which the counts are regressed on a set of observed individual characteristics such as age, gender, or nationality.... We propose a generalized event count model to simultaneously allow for a wide class of count data models and account for over- and underdispersion. This model is successfully applied to German data on fertility, divorces and mobility." (SUMMARY IN FRE)},
author = {Winkelmann, R and Zimmermann, K F},
booktitle = {Mathematical population studies},
doi = {10.1080/08898489409525374},
file = {:home/eduardo/Documents/Mendeley Desktop/41{\_}CountDataModel{\_}MathematicalPopulationStudies{\_}1993.pdf:pdf},
isbn = {9780470510247},
issn = {0889-8480},
keywords = {Demographic Factors,Developed Countries,Divorce,Estimation Technics,Europe,Fertility,Germany,Mathematical Model,Migration,Models,Nuptiality,Population,Population Dynamics,Research Methodology,Theoretical,Western Europe},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
number = {3},
pages = {205--221, 223},
pmid = {12287090},
title = {{Count data models for demographic data}},
volume = {4},
year = {1994}
}
@article{Winkelmann1995,
author = {Winkelmann, Rainer},
doi = {10.1080/07350015.1995.10524620},
file = {:home/eduardo/Documents/Mendeley Desktop/winkelmann1995.pdf:pdf},
issn = {0735-0015},
journal = {Journal of Business {\&} Economic Statistics},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
month = {oct},
number = {4},
pages = {467--474},
title = {{Duration Dependence and Dispersion in Count-Data Models}},
url = {http://www.tandfonline.com/doi/abs/10.1080/07350015.1995.10524620},
volume = {13},
year = {1995}
}
@book{Winkelmann2008,
address = {Berlin, Heidelberg},
author = {Winkelmann, Rainer},
booktitle = {Vasa},
doi = {10.1007/978-3-540-78389-3},
file = {:home/eduardo/Documents/Mendeley Desktop/Winkelmann - 2008 - Econometric Analysis of Count Data.pdf:pdf},
isbn = {978-3-540-77648-2},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
pages = {342},
publisher = {Springer Berlin Heidelberg},
title = {{Econometric Analysis of Count Data}},
url = {http://medcontent.metapress.com/index/A65RM03P4874243N.pdf http://link.springer.com/10.1007/978-3-540-78389-3},
year = {2008}
}
@article{Zeileis2007,
abstract = {To offer a practical demonstration of regression models recommended for count outcomes using longitudinal predictors of children's medically attended injuries.},
author = {Zeileis, Achim and Kleiber, Christian and Jackman, Simon},
doi = {10.1093/jpepsy/jsn055},
file = {:home/eduardo/Documents/Mendeley Desktop/Zeileis, Kleiber, Jackman - 2007 - Regression Models for Count Data in R.pdf:pdf},
isbn = {1548-7660},
issn = {1465735X},
journal = {Journal Of Statistical Software},
keywords = {glm,hurdle model,negative binomial model,poisson model,zero inflated model},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
number = {8},
pages = {1076--84},
pmid = {18522994},
title = {{Regression Models for Count Data in R}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21518631},
volume = {27},
year = {2007}
}
@article{Zeviani2014,
abstract = {Event counts are response variables with non-negative integer values representing the number of times that an event occurs within a fixed domain such as a time interval, a geographical area or a cell of a contingency table. Analysis of counts by Gaussian regression models ignores the discreteness, asymmetry and heteroscedasticity and is inefficient, providing unrealistic standard errors or possibly negative predictions of the expected number of events. The Poisson regression is the standard model for count data with underlying assumptions on the generating process which may be implausible in many applications. Statisticians have long recognized the limitation of imposing equidispersion under the Poisson regression model. A typical situation is when the conditional variance exceeds the conditional mean, in which case models allowing for overdispersion are routinely used. Less reported is the case of underdispersion with fewer modeling alternatives and assessments available in the literature. One of such alternatives, the Gamma-count model, is adopted here in the analysis of an agronomic experiment designed to investigate the effect of levels of defoliation on different phenological states upon the number of cotton bolls. Data set and code for analysis are available as online supplements. Results show improvements over the Poisson model and the semi-parametric quasi-Poisson model in capturing the observed variability in the data. Estimating rather than assuming the underlying variance process leads to important insights into the process. Event counts are response variables with non-negative integer values representing the number of times that an event occurs within a fixed domain such as a time interval, a geographical area or a cell of a contingency table. Analysis of counts by Gaussian regression models ignores the discreteness, asymmetry and heteroscedasticity and is inefficient, providing unrealistic standard errors or possibly negative predictions of the expected number of events. The Poisson regression is the standard model for count data with underlying assumptions on the generating process which may be implausible in many applications. Statisticians have long recognized the limitation of imposing equidispersion under the Poisson regression model. A typical situation is when the conditional variance exceeds the conditional mean, in which case models allowing for overdispersion are routinely used. Less reported is the case of underdispersion with fewer modeling alternatives and assessments available in the literature. One of such alternatives, the Gamma-count model, is adopted here in the analysis of an agronomic experiment designed to investigate the effect of levels of defoliation on different phenological states upon the number of cotton bolls. Data set and code for analysis are available as online supplements. Results show improvements over the Poisson model and the semi-parametric quasi-Poisson model in capturing the observed variability in the data. Estimating rather than assuming the underlying variance process leads to important insights into the process.},
author = {Zeviani, Walmes Marques and {Ribeiro Jr}, Paulo Justiniano and Bonat, Wagner Hugo and Shimakura, Silvia Emiko and Muniz, Joel Augusto},
doi = {10.1080/02664763.2014.922168},
file = {:home/eduardo/Documents/Mendeley Desktop/Zeviani et al. - 2014 - The Gamma-count distribution in the analysis of experimental underdispersed data.pdf:pdf;:home/eduardo/Documents/Mendeley Desktop/Zeviani et al. - 2014 - The Gamma-count distribution in the analysis of experimental underdispersed data(2).pdf:pdf},
issn = {0266-4763},
journal = {Journal of Applied Statistics},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
number = {October},
pages = {1--11},
title = {{The Gamma-count distribution in the analysis of experimental underdispersed data}},
url = {http://dx.doi.org/10.1080/02664763.2014.922168},
year = {2014}
}
@misc{Martelli2008,
address = {Uberl{\^{a}}ndia- MG},
author = {Martelli, T. and Matoso, A. O. and Queir{\'{o}}z, M. V. B. M. and Potrich, D. C. and da Silva, A. M. and Degrande, P. E.},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
publisher = {XXII Congresso Brasileiro de Entomologia},
title = {{Influ{\^{e}}ncia do ataque de mosca-branca Bemisia tabaci Biotipo B, nos {\'{i}}ndices de produtividade do algodoeiro}},
year = {2008}
}
@misc{UCLA,
author = {UCLA, Statistical Consulting Group},
mendeley-groups = {TCC{\_}UFPR{\_}2015},
title = {{Data Analysis Examples}},
year = {2015},
url = {http://www.ats.ucla.edu/stat/dae/}
}