3 resultados para covariance analysis
em Repositório digital da Fundação Getúlio Vargas - FGV
Resumo:
O presente trabalho descreve o estudo comparativo entre dois métodos de ensino aplicados à disciplina de Fisiologia Cárdio-respiratória do curso de Graduação em Medicina, no Centro de Ciências Médicas da Universidade Federal do Rio de Janeiro. Os métodos comparados foram: a auto-instrução e o método tradicional. A formulação do problema, o seu contexto e fundamentação teórica são descritos no início do trabalho, que prossegue' presentando o planejamento do curso com o emprego de ambos os métodos. Em seguida, descreve-se a metodologia utilizada no estudo experimental. Foi adotado o esquema de grupos equivalentes com pós-teste, sendo que o grupo experimental e os grupos de controle' foram escolhidos aleatoriamente. A hipótese experimental visava comprovar que a nota ' final', correspondente à verificação da aprendizagem na disciplina, apresenta diferença significativa entre os alunos que foram submetidos ao método de auto-instrução, comparativamente à nota dos alunos que foram submetidos ao método tradicional. O tratamento estatístico utilizado foi a análise da covariância com o nível de significância de 0,05. O resultado da análise da covariância não foi significativo, considerando a média final do aluno no teste-critério, assim como as notas parciais nas cinco semanas do curso. Uma análise de regressão por passos foi feita, visando controlar algumas variáveis pudessem intervir na diferença entre os grupos experimental e de controle. Entre as variáveis escolhidas, pode-se afirmar que é preditora da nota do aluno na disciplina Fisiologia Cárdio-respiratória, a nota anterior do aluno na disciplina Biofísica. Concluindo, sugere-se novas pesquisas no campo, principalmente relativas a tempo efetivamente gasto pelo professor e pelo aluno, utilizando o método de auto-instrução, assim como' medidas de retenção da aprendizagem.
Resumo:
We examine bivariate extensions of Aït-Sahalia’s approach to the estimation of univariate diffusions. Our message is that extending his idea to a bivariate setting is not straightforward. In higher dimensions, as opposed to the univariate case, the elements of the Itô and Fokker-Planck representations do not coincide; and, even imposing sensible assumptions on the marginal drifts and volatilities is not sufficient to obtain direct generalisations. We develop exploratory estimation and testing procedures, by parametrizing the drifts of both component processes and setting restrictions on the terms of either the Itô or the Fokker-Planck covariance matrices. This may lead to highly nonlinear ordinary differential equations, where the definition of boundary conditions is crucial. For the methods developed, the Fokker-Planck representation seems more tractable than the Itô’s. Questions for further research include the design of regularity conditions on the time series dependence in the data, the kernels actually used and the bandwidths, to obtain asymptotic properties for the estimators proposed. A particular case seems promising: “causal bivariate models” in which only one of the diffusions contributes to the volatility of the other. Hedging strategies which estimate separately the univariate diffusions at stake may thus be improved.
Resumo:
This paper uses an output oriented Data Envelopment Analysis (DEA) measure of technical efficiency to assess the technical efficiencies of the Brazilian banking system. Four approaches to estimation are compared in order to assess the significance of factors affecting inefficiency. These are nonparametric Analysis of Covariance, maximum likelihood using a family of exponential distributions, maximum likelihood using a family of truncated normal distributions, and the normal Tobit model. The sole focus of the paper is on a combined measure of output and the data analyzed refers to the year 2001. The factors of interest in the analysis and likely to affect efficiency are bank nature (multiple and commercial), bank type (credit, business, bursary and retail), bank size (large, medium, small and micro), bank control (private and public), bank origin (domestic and foreign), and non-performing loans. The latter is a measure of bank risk. All quantitative variables, including non-performing loans, are measured on a per employee basis. The best fits to the data are provided by the exponential family and the nonparametric Analysis of Covariance. The significance of a factor however varies according to the model fit although it can be said that there is some agreements between the best models. A highly significant association in all models fitted is observed only for nonperforming loans. The nonparametric Analysis of Covariance is more consistent with the inefficiency median responses observed for the qualitative factors. The findings of the analysis reinforce the significant association of the level of bank inefficiency, measured by DEA residuals, with the risk of bank failure.