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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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Book ChapterDOI

Problems in the Analysis of Environmental Monitoring Data

TL;DR: In this paper, the authors discuss problems in the statistical analysis of data that monitors the environmental effects of planned human alterations, and argue that the right questions in impact assessment have only approximate and possibly multiple answers, requiring informal combinations of formal results.
Journal ArticleDOI

Effective Number of Observations and Unbiased Estimators of Variance for Autocorrelated Data - an Overview

TL;DR: In this article, the authors present an extension of the GUM formulation of type-A uncertainty for the case of autocorrelated observations, which can be used to express 2 a s and ) ( 2 x sa in a simple manner.
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Movement-activated myoclonus in genetically defined progressive myoclonic epilepsies: EEG-EMG relationship estimated using autoregressive models.

TL;DR: AR models were capable of describing EEG-EMG relationships in patients with PME, and indicated that coherent cortical and EMG beta oscillations are crucially involved in the generation of myoclonus.
Journal ArticleDOI

Testing for common deterministic trend slopes

TL;DR: In this paper, the authors propose tests for hypotheses on the parameters for deterministic trends and apply their tests to examine real GDP convergence for a sample of seven European countries, and derive the asymptotic theory and provide some relevant critical values.
Journal Article

On the boundary kernel method for non-parametric curve estimation near endpoints

TL;DR: It is shown that common kernel functions which satisfy some mild requirements can be derived as the solution of a variational problem involving a certain weight function and its solution can be extended to cover boundary kernels.
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