Nonparametric estimation of residual variance revisited
نویسندگان
چکیده
منابع مشابه
Bayesian Nonparametric Estimation of Ex-post Variance
Variance estimation is central to many questions in finance and economics. Until now ex-post variance estimation has been based on infill asymptotic assumptions that exploit high-frequency data. This paper offers a new exact finite sample approach to estimating ex-post variance using Bayesian nonparametric methods. In contrast to the classical counterpart, the proposed method exploits pooling o...
متن کاملNonparametric Estimation of Residual Moments and Covariance
Abstract: The aim of nonparametric regression is to model the behaviour of a response vector Y in terms of an explanatory vector X , based only on a finite set of empirical observations. This is usually performed under the additive hypothesis Y = f(X) + R, where f(X) = E(Y |X) is the true regression function and R is the true residual variable. Subject to a Lipschitz condition on f , we propose...
متن کاملAutoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation
We propose an automatic methodology framework for shortand long-term prediction of time series by means of fuzzy inference systems. In this methodology, fuzzy techniques and statistical techniques for nonparametric residual variance estimation are combined in order to build autoregressive predictive models implemented as fuzzy inference systems. Nonparametric residual variance estimation plays ...
متن کاملVariance 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 کاملNonparametric variance function estimation with missing data
In this paper a fixed design regression model where the errors follow a strictly stationary process is considered. In this model the conditional mean function and the conditional variance function are unknown curves. Correlated errors when observations are missing in the response variable are assumed. Four nonparametric estimators of the conditional variance function based on local polynomial f...
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: Biometrika
سال: 1993
ISSN: 0006-3444,1464-3510
DOI: 10.1093/biomet/80.2.373