Métodos utilizados na avaliação psicofísica da visão de cores humana

A cor é um atributo perceptual que nos permite identificar e localizar padrões ambientais de mesmo brilho e constitui uma dimensão adicional na identificação de objetos, além da detecção de inúmeros outros atributos dos objetos em sua relação com a cena visual, como luminância, contraste, forma, movimento, textura, profundidade. Decorre daí a sua importância fundamental nas atividades desempenhadas pelos animais e pelos seres humanos em sua interação com o ambiente. A psicofísica visual preocupa-se com o estudo quantitativo da relação entre eventos físicos de estimulação sensorial e a resposta comportamental resultante desta estimulação, fornecendo dessa maneira meios de avaliar aspectos da visão humana, como a visão de cores. Este artigo tem o objetivo de mostrar diversas técnicas eficientes na avaliação da visão cromática humana através de métodos psicofísicos adaptativos.

Heteroscedastic Nonlinear Regression Models

In this article, we present a generalization of the Bayesian methodology introduced by Cepeda and Gamerman (2001) for modeling variance heterogeneity in normal regression models where we have orthogonality between mean and variance parameters to the general case considering both linear and highly nonlinear regression models. Under the Bayesian paradigm, we use MCMC methods to simulate samples for the joint posterior distribution. We illustrate this algorithm considering a simulated data set and also considering a real data set related to school attendance rate for children in Colombia. Finally, we present some extensions of the proposed MCMC algorithm.

Regression Models with Heteroscedasticity using Bayesian Approach

In this paper, we compare the performance of two statistical approaches for the analysis of data obtained from the social research area. In the first approach, we use normal models with joint regression modelling for the mean and for the variance heterogeneity. In the second approach, we use hierarchical models. In the first case, individual and social variables are included in the regression modelling for the mean and for the variance, as explanatory variables, while in the second case, the variance at level 1 of the hierarchical model depends on the individuals (age of the individuals), and in the level 2 of the hierarchical model, the variance is assumed to change according to socioeconomic stratum. Applying these methodologies, we analyze a Colombian tallness data set to find differences that can be explained by socioeconomic conditions. We also present some theoretical and empirical results concerning the two models. From this comparative study, we conclude that it is better to jointly modelling the mean and variance heterogeneity in all cases. We also observe that the convergence of the Gibbs sampling chain used in the Markov Chain Monte Carlo method for the jointly modeling the mean and variance heterogeneity is quickly achieved.