Smoothing Splines: Regression, Derivatives and Deconvolution
نویسندگان
چکیده
منابع مشابه
Smoothing Splines Estimators for Functional Linear Regression
The paper considers functional linear regression, where scalar responses Y1, . . . , Yn are modeled in dependence of random functions X1, . . . ,Xn. We propose a smoothing splines estimator for the functional slope parameter based on a slight modification of the usual penalty. Theoretical analysis concentrates on the error in an out-ofsample prediction of the response for a new random function ...
متن کاملBayesian Smoothing and Regression Splines for Measurement Error Problems
In the presence of covariate measurement error, estimating a regression function nonparametrically is extremely dif cult, the problem being related to deconvolution. Various frequentist approaches exist for this problem, but to date there has been no Bayesian treatment. In this article we describe Bayesian approaches to modeling a exible regression function when the predictor variable is mea...
متن کاملRegression with Ordered Predictors via Ordinal Smoothing Splines
Many applied studies collect one or more ordered categorical predictors, which do not fit neatly within classic regression frameworks. In most cases, ordinal predictors are treated as either nominal (unordered) variables or metric (continuous) variables in regression models, which is theoretically and/or computationally undesirable. In this paper, we discuss the benefit of taking a smoothing sp...
متن کاملEstimation of the Functional Linear Regression with Smoothing Splines
We consider functional linear regression where a real variable Y depends on a functional variable X. The functional coefficient of the model is estimated by means of smoothing splines. We derive the rates of convergence with respect to the semi-norm induced by the covariance operator of X, which comes to evaluate the error of prediction. These rates, which essentially depend on the smoothness o...
متن کاملImproved inference in nonparametric regression using Lk-Smoothing splines
Smoothing splines are one of the most popular approaches to nonparametric regression. Wahba (J. Roy. Statist. Soc. Set. B 40 (1978) 364-372; 45 (1983) 133-150) showed that smoothing splines are also Bayes estimates and used the corresponding prior model to derive interval estimates for the regression function. Although the interval estimates work well on a global basis, they can have poor local...
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: 1983
ISSN: 0090-5364
DOI: 10.1214/aos/1176346065