Modelos não-lineares para dados longitudinais provenientes de experimentos em blocos casualizados abordagem bayesiana / Nonlinear models for longitudinal data from experiments in randomized block design a bayesian framework
Everton Batista da Rocha
DATA DE PUBLICAÇÃO
Data consisting of repeated measurements taken on each of a number of individual arise commonly in agricultural and biological applications. Modeling data of this kind usually involves the characterization of the relationship between the measured response and covariate. In many application,the proposed systematic relationship between the measured response is nonlinear in unknown parameters of interest. For example, in growing studies of trees, generally the behavior of the response variable over time is best described by a nonlinear model in the parameters of interest because this model characterizes better the reality of biological phenomenon in study and because is possible to do a biological interpretation of the parameters. The presence of repeated observations on an individual requires particular care in characterizing the random variation among measurements within a given individual and random variation among individuals. Likely the observations made on the same unit are correlated, probability decreasing over time and possible the variances are growth among the serial measurements. In this work we considerer two covariance structure namely: independent random error vectors whose elements are also independent with mean zero and variance 2, but this formulation does not incorporate possible dependence among the observation taken on the same subject neither that in longitudinal studies it is quite common to have the variances varying along the ordered dimension. Therefore, it is important to have models that allow for both dependences (within and between subjects) and also for heteroscedasticity in their formulations. Then we considerer other covariance structure namely: the structure is a non structure which permit that the data set \tells"about the covariance structure. In this work we analyzed a randomized block design assuming a three-stage Bayesian hierarchical model. On the rst stage, we model the intra-individual variation, on the second stage, we model the inter-individual variation. This stage of hierarchy gives an explicit relationship between the random parameters. On the third stage, we dene the hyperprior distribution to incorporate the uncertainty about the unknown parameters. For the statistical analysis we used a data set 12 from a experiment conducted at Klabin Fabricadora de Papel e Celulose S.A. from Parana, Brazil, involving two Eucalyptus species and two spacings in a complete randomized design; where the response variable, dened as the solid volume with bark, was evaluated for each of 16 subjects (groups of Eucalyptus trees), and four subjects were randomly assigned to one of four treatments. To represent the expected growing function of the Eucalyptuss tree Gompertz nonlinear model was used. Using the Gompertz nonlinear model is possible to a biological interpretation of the parameters. Considering dierent structures covariance within subjects, a program for the analysis of the data set was implemented in WinBUGS.
análise de dados longitudinais bayesian inference block design. curvas de crescimento delineamento experimental eucalipto eucalyptus experimental design growth curves inferência bayesiana longitudinal data analysis modelos não lineares (planejamento e pesquisa) nonlinear models (design and research) planejamento em blocos.
- Modelos não-lineares para analise de dados longitudinais
- Non linear models for count longitudinal data
- Modelos lineares generalizados mistos para dados longitudinais.
- Uso dos métodos clássico e bayesiano para os modelos não-lineares heterocedásticos simétricos
- Ajuste de modelos estocásticos lineares e não-lineares para a descrição do perfil longitudinal de árvores