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Journal Article

Spectral Analysis and Time Series

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TLDR
In this article, the authors introduce the concept of Stationary Random Processes and Spectral Analysis in the Time Domain and Frequency Domain, and present an analysis of Processes with Mixed Spectra.
Abstract
Preface. Preface to Volume 2. Contents of Volume 2. List of Main Notation. Basic Concepts. Elements of Probability Theory. Stationary Random Processes. Spectral Analysis. Estimation in the Time Domain. Estimation in the Frequency Domain. Spectral Analysis in Practice. Analysis of Processes with Mixed Spectra.

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Bootstrap technology and applications

TL;DR: Some bootstrap resampling methods are reviewed, emphasizing applications through illustrations with some real data, and special attention is given to regression, problems with dependentData, and choosing tuning parameters for optimal performance.
Book

Least-squares variance component estimation: theory and GPS applications

TL;DR: In this paper, the LS-VCE method is used to estimate the covariance matrix of the (co) variance component, and the minimum variance estimator of the variance component is obtained by choosing the weight matrix as the inverse of the covariation matrix.
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Using wavelets to obtain a consistent ordinary least squares estimator of the long-memory parameter

TL;DR: In this paper, a log-linear relationship between the wavelet coefficients' variance and the scaling parameter equal to the long memory parameter is established, which yields a consistent OLS estimator of the long-memory parameter.
Posted Content

The Statistical Properties Of Dimension Calculations Using Small Data Sets: Some Economic Applications

TL;DR: In this article, the authors show that, with the techniques available to date and for the time series examined so far, there is virtually no evidence for the presence of simple chaotic attractors.
Journal ArticleDOI

A new statistical approach to detecting significant activation in functional MRI.

TL;DR: It is shown that for experimental designs with periodic stimuli, only a few aspects of the serial dependence are important and these can be estimated reliably via nonparametric estimation of the spectral density of the time series, whereas existing techniques are biased by their assumptions.
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