Penalized spline estimation for functional coefficient regression models
نویسندگان
چکیده
منابع مشابه
Bootstrapping for Penalized Spline Regression∗†‡
We describe and contrast several different bootstrapping procedures for penalized spline smoothers. The bootstrapping procedures considered are variations on existing methods, developed under two different probabilistic frameworks. Under the first framework, penalized spline regression is considered an estimation technique to find an unknown smooth function. The smooth function is represented i...
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 کاملExistence and Uniqueness of Penalized Least Square Estimation for Smoothing Spline Nonlinear Nonparametric Regression Models
where Ni are known nonlinear functionals, g = (g1, · · · , gr) are unknown functions, and 2i iid ∼ N(0, σ) are random errors. Without loss of generality, we assume that r = 2. As in O’Sullivan (1990), we express design points x explicitly in the functional Ni: Ni(g1, g2) = η(g1, g2; xi), where η is a known nonlinear functional. In the following sections, η(g1, g2; x) is sometimes also represent...
متن کاملPenalized spline models for functional principal component analysis
We propose an iterative estimation procedure for performing functional principal component analysis. The procedure aims at functional or longitudinal data where the repeated measurements from the same subject are correlated. An increasingly popular smoothing approach, penalized spline regression, is used to represent the mean function. This allows straightforward incorporation of covariates and...
متن کاملSieve Instrumental Variable Quantile Regression Estimation of Functional Coefficient Models∗
In this paper, we consider sieve instrumental variable quantile regression (IVQR) estimation of functional coefficient models where the coefficients of endogenous regressors are unknown functions of some exogenous covariates. We approximate the unknown functional coefficients by some basis functions and estimate them by the IVQR technique. We establish the uniform consistency and asymptotic nor...
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: Computational Statistics & Data Analysis
سال: 2010
ISSN: 0167-9473
DOI: 10.1016/j.csda.2009.09.036