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 …

estimated via local polynomials with loess or kernel weighting, asymptotic theory is derived for the latter. In keeping with the … without deriving asymptotic theory on a case-by-case basis. The methods are applied to a series of examples. The application … to nutrition is called "nonparametric calibration" after the term used for studies in that field. Other applications …

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 …

estimation ; minimax regret ; ANOVA ; discrete explanatory variable ; twoway classification …

equivalent sequence in the density estimation model with i. i. d. observations. The recipe is, to put it roughly, to calculate … distance ; Markov kernel ; curve estimation …