Local adaptive smoothing in kernel regression estimation
نویسندگان
چکیده
منابع مشابه
Adaptive Kernel Smoothing Regression for Spatio-Temporal Environmental Datasets
This paper describes a method for performing kernel smoothing regression in an incremental, adaptive manner. A simple and fast combination of incremental vector quantization with kernel smoothing regression using adaptive bandwidth is shown to be effective for online modeling of environmental datasets. The method is illustrated on openly available datasets corresponding to the Tropical Atmosphe...
متن کاملFunction Estimation Using Data Adaptive Kernel Smoothers - How Much Smoothing?
We consider a common problem in physics: How to estimate a smooth function given noisy measurements. We assume that the unknown signal is measured at N different times, {ti: i = 1, . . . N} and that the measurements, {yi}, have been contaminated by additive noise. Thus the measurements satisfy yi = g(ti) + i, where g(t) is the unknown signal and i are random errors. For simplicity, we assume th...
متن کاملOn kernel smoothing for extremal quantile regression
Nonparametric regression quantiles obtained by inverting a kernel estimator of the conditional distribution of the response are long established in statistics. Attention has been, however, restricted to ordinary quantiles staying away from the tails of the conditional distribution. The purpose of this paper is to extend their asymptotic theory far enough into the tails. We focus on extremal qua...
متن کاملMultistep kernel regression smoothing by boosting
In this paper we propose a simple multistep regression smoother which is constructed in a boosting fashion, by learning the Nadaraya–Watson estimator with L2Boosting. Differently from the usual approach, we do not focus on L2Boosting for ever. Given a kernel smoother as a learner, we explore the boosting capability to build estimators using a finite number of boosting iterations. This approach ...
متن کاملOn adaptive smoothing in kernel discriminant analysis
One popular application of kernel density estimation is in kernel discriminant analysis, where kernel estimates of population densities are plugged in the Bayes rule to develop a nonparametric classifier. Performance of these kernel density estimates and that of the corresponding classifier depend on the values of associated smoothing parameters commonly known as the bandwidths. Bandwidths that...
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: Statistics & Probability Letters
سال: 2010
ISSN: 0167-7152
DOI: 10.1016/j.spl.2009.12.008