Sequential robust estimation for nonparametric autoregressive models

Nonparametric Estimation of Volatility Models with General Autoregressive Innovations

We are interested in modeling a zero mean heteroscedastic time series process with autoregressive error process of finite known order p. The model can be used for testing a martingale difference sequence hypothesis that is often adopted uncritically in financial time series against a fairly general alternative. When the argument is deterministic, we provide an innovative nonparametric estimator...

On nonergodicity for nonparametric autoregressive models

*Correspondence: [email protected] School of Science, Jiangxi University of Science and Technology, Ganzhou, 341000, China Abstract In this paper, we introduce a class of nonlinear time series models with random time delay under random environment, sufficient conditions for nonergodicity of these models are developed. The so-called Markovnization methods are used, that is, proper supplem...

Sequential nonparametric density estimation

A Robust Nonparametric Estimation Framework for Implicit Image Models

Robust model fitting is important for computer vision tasks due to the occurrence of multiple model instances, and, unknown nature of noise. The linear errors-in-variables (EIV) model is frequently used in computer vision for model fitting tasks. This paper presents a novel formalism to solve the problem of robust model fitting using the linear EIV framework. We use Parzen windows to estimate t...

Autoregressive coefficient estimation in nonparametric analysis

The article considers the Yule-Walker estimator of the autoregressive coefficient based on the observed time series that contains an unknown trend function and an autoregressive error term. The trend function is estimated by means of B-splines and then subtracted from the observations. The Yule-Walker estimator is obtained from the residual sequence. Asymptotic properties of this estimator are ...