Showing 1 - 8 of 8
This paper describes an estimator of the additive components of a nonparametric additive model with a known link function. When the additive components are twice continuously differentiable, the estimator is asymptotically normally distributed with a rate of convergence in probability of n -2/5...
Persistent link: https://www.econbiz.de/10010310569
This paper describes an estimator of the additive components of a nonparametric additive model with a known link function. When the additive components are twice continuously differentiable, the estimator is asymptotically normally distributed with a rate of convergence in probability of n -2/5...
Persistent link: https://www.econbiz.de/10010956560
We derive the asymptotic distribution of a new backfitting procedure for estimating the closest additive approximation to a nonparametric regression function. The procedure employs a recent projection interpretation of popular kernel estimators provided by Mammen et al. (1997), and the...
Persistent link: https://www.econbiz.de/10005249163
We derive the asymptotic distribution of a new backfitting procedure for estimating the closest additive approximation to a nonparametric regression function. The procedure employs a recent projection interpretation of popular kernel estimators provided by Mammen, Marron, Turlach and Wand...
Persistent link: https://www.econbiz.de/10005310381
This paper is concerned with estimating the additive components of a nonparametric additive quantile regression model. We develop an estimator that is asymptotically normally distributed with a rate of convergence in probability of $n^{-r/(2r+1)}$ when the additive components are $r$-times...
Persistent link: https://www.econbiz.de/10005342359
We derive the asymptotic distribution of a new backfitting procedure for estimating the closest additive approximation to a nonparametric regression function. The procedure employs a recent projection interpretation of popular kernel estimators provided by Mammen, Marron, Turlach and Wand...
Persistent link: https://www.econbiz.de/10010744974
When data are affected by multicollinearity in the linear regression framework, then concurvity will be present in fitting a generalized additive model (GAM). The term concurvity describes nonlinear dependencies among the predictor variables. As collinearity results in inflated variance of the...
Persistent link: https://www.econbiz.de/10011124497
Persistent link: https://www.econbiz.de/10005390535