regression model. Asymptotic distribution theory is developed for the estimation method which enjoys the same rate of convergence … as univariate function estimation. For the test statistic, asymptotic normal theory is established. These theoretical …We propose marginal integration estimation and testing methods for the coefficients of varying coefficient multivariate …

dealing with more than one lag. When the mean has an additive structure, however, better estimation methods are available … estimation of the conditional mean, it is equally if not more important to measure the future risk of the series along with the … interpretation of each lagged value's influence on such a function. In this paper we consider the joint estimation of both the …

techniques, regression splines and kernel estimation, of which both can be used in the presence of measurement error. Within the … ; Measurement Error ; Local Polynomial Regression ; SIMEX ; Asymptotic theory ; Estimating Equations ; Nonlinear Regression … ; Bandwidth Selection ; Regression Splines ; Sandwich Estimation …

from a nutrition study are analyzed using the methods. Finally, the results from a simulation study are presented and the … performances of the methods compared. -- Bootstrap ; Measurement Error ; Errors-in-Variables ; Asymptotic theory ; Estimating … Equations ; Nonlinear Regression ; Nutrition …

Estimation …

We introduce a new method for the estimation of discount functions, yield curves and forward curves from government … although we do show how to impose various restrictions in the estimation. Our method is based on Kernel smoothing and is … our estimation procedure is iterative, rather like the backfitting method of estimating non-parametric models. We …