We study smooth backfitting when there are errors-in-variables, which is motivated by functional additive models for a functional regression model with a scalar response and multiple functional predictors that are additive in the functional principal components of the predictor processes. The development of a new smooth backfitting technique for the estimation of the additive component function...

We consider regression when the predictor is measured with error and an instrumental variable is available. The regression function can be modeled linearly, nonlinearly, or nonparametrically. Our major new result shows that the regression function and all parameters in the measurement error model are identified under relatively weak conditions, much weaker than previously known to imply identif...

abstract background: although studies have demonstrated significant negative relationships between quality of life (qol), fatigue, and the most common psychological symptoms (depression, anxiety, stress), the main ambiguity of previous studies on qol is in the relative importance of these predictors. also, there is lack of adequate knowledge about the actual contribution of each of them in the ...

For the normal-error multiple linear regression model, the problem considered is the construction of a simultaneous confidence band with width proportional to the standard error for the regression function when the predictor variables are constrained in intervals. To date, exact band is available only when there is one predictor variable. When there are more than one predictor variables, only c...

Two experiments are reported in which four dependent variables; typicality ratings, response times, category-naming frequencies, and exemplar-generation frequencies of natural language concepts, were predicted by two sorts of prototype predictors and by an exemplar predictor related to Heit and Barsalou’s (1996) instantiation principle. In the first experiment, the exemplar predictor was compar...