Nonparametric Approach for Non-Gaussian Vector Stationary Processes
نویسندگان
چکیده
منابع مشابه
Empirical Likelihood Approach for Non-Gaussian Locally Stationary Processes
An application of empirical likelihood method to non-Gaussian locally stationary processes is presented. Based on the central limit theorem for locally stationary processes, we calculate the asymptotic distribution of empirical likelihood ratio statistics. It is shown that empirical likelihood method enables us to make inference on various important indices in time series analysis. Furthermore,...
متن کاملEmpirical Likelihood Approach for Non Gaussian Stationary Processes
A. For a class of non Gaussian stationary processes, we develop the empirical likelihood approach. For this it is known that Whittle’s likelihood is the most fundamental tool to get a good estimator of unknown parameter, and that the score functions are asymptotically chi-square distributed. Motivated by the Whittle likelihood, we apply the empirical likelihood approach to its derivative...
متن کاملEmpirical Likelihood Approach for Non-Gaussian Vector Stationary Processes and Its Application to Minimum Contrast Estimation
A. For a class of vector-valued non-Gaussian stationary processes with unknown parameters, we develop the empirical likelihood approach. In time series analysis it is known that Whittle likelihood is one of the most fundamental tools to get a good estimator of unknown parameters, and that the score functions are asymptotically normal. Motivated by the Whittle likelihood, we apply the emp...
متن کاملLearning Stationary Time Series using Gaussian Processes with Nonparametric Kernels
We introduce the Gaussian Process Convolution Model (GPCM), a two-stage nonparametric generative procedure to model stationary signals as the convolution between a continuous-time white-noise process and a continuous-time linear filter drawn from Gaussian process. The GPCM is a continuous-time nonparametricwindow moving average process and, conditionally, is itself a Gaussian process with a non...
متن کاملNonparametric Estimation for Stationary Processes
We consider the kernel density and regression estimation problem for a wide class of causal processes. Asymptotic normality of the kernel estimators is established under minimal regularity conditions on bandwidths. Optimal uniform error bounds are obtained without imposing strong mixing conditions. The proposed method is based on martingale approximations and provides a unified framework for no...
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: Journal of Multivariate Analysis
سال: 1996
ISSN: 0047-259X
DOI: 10.1006/jmva.1996.0014