Spline-backfitted kernel smoothing of nonlinear additive autoregression model
نویسندگان
چکیده
منابع مشابه
Spline-backfitted Kernel Smoothing of Nonlinear Additive Autoregression Model
Application of nonand semiparametric regression techniques to high dimensional time series data have been hampered due to the lack of effective tools to address the “curse of dimensionality”. Under rather weak conditions, we propose spline-backfitted kernel estimators of the component functions for the nonlinear additive time series data that is both computationally expedient so it is usable fo...
متن کاملSpline-backfitted kernel smoothing of partially linear additive model
A spline-backfitted kernel smoothing method is proposed for partially linear additive model. Under assumptions of stationarity and geometric mixing, the proposed function and parameter estimators are oracally efficient and fast to compute. Such superior properties are achieved by applying to the data spline smoothing and kernel smoothing consecutively. Simulation experiments with both moderate ...
متن کاملSpline-backfitted Kernel Smoothing of Additive Coefficient Model
Additive coefficient model (Xue and Yang 2006a, b) is a flexible tool for multivariate regression and time series analysis that circumvents the “curse of dimensionality.” We propose spline-backfitted kernel (SBK) and spline-backfitted local linear (SBLL) estimators for the component functions in the additive coefficient model that is both (i) computationally expedient so it is usable for analyz...
متن کاملEfficient and fast spline-backfitted kernel smoothing of additive models
A great deal of effort has been devoted to the inference of additive model in the last decade. Among existing procedures, the kernel type are too costly to implement for high dimensions or large sample sizes, while the spline type provide no asymptotic distribution or uniform convergence. We propose a one step backfitting estimator of the component function in an additive regression model, usin...
متن کاملEfficient and Fast Spline-backfitted Kernel Smoothing of Additive Regression Model∗
A great deal of efforts has been devoted to the inference of additive model in the last decade. Among the many existing procedures, the kernel type are too costly to implement for large number of variables or for large sample sizes, while the spline type provide no asymptotic distribution or any measure of uniform accuracy. We propose a synthetic estimator of the component function in an additi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2007
ISSN: 0090-5364
DOI: 10.1214/009053607000000488