• português (Brasil)
    • English
    • español
  • English 
    • português (Brasil)
    • English
    • español
  • Login
About
  • Policies
  • Instructions to authors
  • Contact
    • Policies
    • Instructions to authors
    • Contact
View Item 
  •   Home
  • Centro de Ciências Exatas e de Tecnologia - CCET
  • Programas de Pós-Graduação
  • Estatística - PPGEs
  • Teses e dissertações
  • View Item
  •   Home
  • Centro de Ciências Exatas e de Tecnologia - CCET
  • Programas de Pós-Graduação
  • Estatística - PPGEs
  • Teses e dissertações
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsAdvisorTitlesSubjectsCNPq SubjectsGraduate ProgramDocument TypeThis CollectionBy Issue DateAuthorsAdvisorTitlesSubjectsCNPq SubjectsGraduate ProgramDocument Type

My Account

Login

Modelo destrutivo com variável terminal em experimentos quimiopreventivos de tumores em animais

Thumbnail
View/Open
4375.pdf (881.8Kb)
Date
2012-04-12
Author
Zavaleta, Katherine Elizabeth Coaguila
Metadata
Show full item record
Abstract
The chemical induction of carcinogens in chemopreventive animal experiments is becoming increasingly frequent in biological research. The purpose of these biological experiments is to evaluate the effect of a particular treatment on the rate of tumors incidence in animals. In this work, the number of promoted tumors per animal will be parametrically modeled following the suggestions given by Kokoska (1987) and Freedman et al. (1993). The study of these chemopreventive experiments will be presented in the context of the destructive model proposed by Rodrigues et al. (2010) with terminal variable that allows or censures the experiment at time of the animal death. Since the data analyzed in this field are subject to excess of zeros (Freedman et al. (1993)), we propose for the number of promoted tumors a negative binomial distribution (NB), a zero-inflated Poisson distribution (ZIP), and a zero-inflated Negative Binomial distribution (ZINB). The selection of these models will be made through the likelihood ratio test and the AIC, BIC criteria. The estimation of its parameters will be obtained by using the method of maximum likelihood, and further simulation studies will also be realized. As a future proposition to finalize this project, it is suggested the Bayesian methodology as an alternative to the method of maximum likelihood via the EM algorithm.
URI
https://repositorio.ufscar.br/handle/ufscar/4561
Collections
  • Teses e dissertações

Related items

Showing items related by title, author, creator and subject.

  • Uma abordagem de teste estrutural de uma transformações M2T baseada em hipergrafos 

    Abade, André da Silva (Universidade Federal de São Carlos, UFSCar, Programa de Pós-Graduação em Ciência da Computação - PPGCC, Câmpus São Carlos, 05/01/2016)
    Context: MDD (Model-Driven Development) is a software development paradigm in which the main artefacts are models, from which source code or other artefacts are generated. Even though MDD allows different views of how to ...
  • Modelos para séries temporais utilizando as distribuições normal generalizada e log-normal generalizada 

    Milani, Eder Angelo (Universidade Federal de São Carlos, UFSCar, Programa de Pós-Graduação em Estatística - PPGEs, Câmpus São Carlos, 23/03/2016)
    From the generalized normal distribution and concepts of the generalized autoregressive moving averages models we introduce the generalized normal-ARMA model as an alternative way to model time series exhibiting symmetry ...
  • Análise de dados longitudinais para variáveis binárias 

    Rodrigues, José Tenylson Gonçalves (Universidade Federal de São Carlos, UFSCar, Programa de Pós-Graduação em Estatística - PPGEs, , 05/03/2009)
    The objective of this work is to present techniques of regression analysis for longitudinal data when the response variable is binary. Initially, there is a review of generalized linear models, marginal models, transition ...

UFSCar
Universidade Federal de São Carlos - UFSCar
Send Feedback

UFSCar

IBICT
 

 


UFSCar
Universidade Federal de São Carlos - UFSCar
Send Feedback

UFSCar

IBICT