The Estimation of Residual Variance in Nonparametric Regression
نویسندگان
چکیده
منابع مشابه
Variance Function Estimation in Multivariate Nonparametric Regression
Variance function estimation in multivariate nonparametric regression is considered and the minimax rate of convergence is established. Our work uses the approach that generalizes the one used in Munk et al (2005) for the constant variance case. As is the case when the number of dimensions d = 1, and very much contrary to the common practice, it is often not desirable to base the estimator of t...
متن کاملOn conditional variance estimation in nonparametric regression
In this paper we consider a nonparametric regression model in which the conditional variance function is assumed to vary smoothly with the predictor. We offer an easily implemented and fully Bayesian approach that involves the Markov chain Monte Carlo sampling of standard distributions. This method is based on a technique utilized by Kim, Shephard, and Chib (1998) for the stochastic volatility ...
متن کاملEstimating residual variance in nonparametric regression using least squares
We propose a new estimator for the error variance in a nonparametric regression model. We estimate the error variance as the intercept in a simple linear regression model with squared differences of paired observations as the dependent variable and squared distances between the paired covariates as the regressor. Our method can be applied to nonparametric regression models with multivariate fun...
متن کاملVariance Estimation in Nonparametric Regression via the Difference Sequence Method
Consider a Gaussian nonparametric regression problem having both an unknown mean function and unknown variance function. This article presents a class of difference-based kernel estimators for the variance function. Optimal convergence rates that are uniform over broad functional classes and bandwidths are fully characterized, and asymptotic normality is also established. We also show that for ...
متن کاملVariance estimation in nonparametric regression via the difference sequence method (short title: Sequence-based variance estimation)
Consider a Gaussian nonparametric regression problem having both an unknown mean function and unknown variance function. This article presents a class of difference-based kernel estimators for the variance function. Optimal convergence rates that are uniform over broad functional classes and bandwidths are fully characterized, and asymptotic normality is also established. We also show that for ...
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: Jurnal Matematika, Statistika dan Komputasi
سال: 2021
ISSN: 2614-8811
DOI: 10.20956/j.v17i3.13192