Penalized spline estimation in the partially linear model
نویسندگان
چکیده
منابع مشابه
Penalized spline estimation in the partially linear model
Penalized Spline Estimation in the Partially Linear Model by Ashley D. Holland Co-Chairs: Matias D. Cattaneo and Virginia R. Young Penalized spline estimators have received considerable attention in recent years because of their good finite-sample performance, especially when the dimension of the regressors is large. In this project, we employ penalized B-splines in the context of the partially...
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 کاملLocal influence analysis for penalized Gaussian likelihood estimation in partially linear single-index models
Single-index model is a potentially tool for multivariate nonparametric regression, generalizes both the generalized linear models(GLM) and the missing-link function problem in GLM. In this paper, we extend Cook’s local influence analysis to the penalized Gaussian likelihood estimator based on P-spline for the partially linear single-index model. Some influence measures, based on the minor pert...
متن کاملNonparametric Small Area Estimation Using Penalized Spline Regression
We propose a new small area estimation approach that combines small area random effects with a smooth, nonparametrically specified trend. By using penalized splines as the representation for the nonparametric trend, it is possible to express the small area estimation problem as a mixed effect regression model. We show how this model can be fitted using existing model fitting approaches such as ...
متن کاملSCAD-Penalized Regression in High-Dimensional Partially Linear Models
We consider the problem of simultaneous variable selection and estimation in partially linear models with a divergent number of covariates in the linear part, under the assumption that the vector of regression coefficients is sparse. We apply the SCAD penalty to achieve sparsity in the linear part and use polynomial splines to estimate the nonparametric component. Under reasonable conditions, i...
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: Journal of Multivariate Analysis
سال: 2017
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2016.10.001