Time Series with Mixed Spectra

Author: Ta-Hsin Li
Publisher: CRC Press
ISBN: 1420010069
Format: PDF, Docs
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Time series with mixed spectra are characterized by hidden periodic components buried in random noise. Despite strong interest in the statistical and signal processing communities, no book offers a comprehensive and up-to-date treatment of the subject. Filling this void, Time Series with Mixed Spectra focuses on the methods and theory for the statistical analysis of time series with mixed spectra. It presents detailed theoretical and empirical analyses of important methods and algorithms. Using both simulated and real-world data to illustrate the analyses, the book discusses periodogram analysis, autoregression, maximum likelihood, and covariance analysis. It considers real- and complex-valued time series, with and without the Gaussian assumption. The author also includes the most recent results on the Laplace and quantile periodograms as extensions of the traditional periodogram. Complete in breadth and depth, this book explains how to perform the spectral analysis of time series data to detect and estimate the hidden periodicities represented by the sinusoidal functions. The book not only extends results from the existing literature but also contains original material, including the asymptotic theory for closely spaced frequencies and the proof of asymptotic normality of the nonlinear least-absolute-deviations frequency estimator.

A New Approach to Time Series with Mixed Spectra

Author: George Ronald Hext
Publisher:
ISBN:
Format: PDF, Mobi
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The time series considered have jumps in their spectral distribution function; that is, the series is the sum of a 'signal' component, comprising a finite linear sum of pure sine-waves, and a 'noise' component, having continuous spectral density function. Given a set of observations from such a time series the primary problem is to estimate the 'signal' frequencies, the power in each component of the signal, and the 'noise' spectral density at these frequencies. The essence of the method used is as follows. For a given set of observations from such a series, and for each frequency that might yield a signal component, several estimates of the spectral density are made, using spectral windows of different bandwidths. To a first approximation, the noise component of the estimate is the same for every window, while the part of the estimate due to the signal is inversely proportional to the bandwidth of the window. Thus using a regression technique, one can separate the signal power from the noise spectral density at the given frequency and estimate these two quantities. These ideas are developed as follows. After a historical introduction, the early part of the thesis is devoted to the 'probability' aspects of the problem. First some results are proved that apply to the 'noise' series or any stationary time series. They give extensions and refinements of early approximations for the expected value of the spectral estimate, and for the covariance between two spectral estimates; these include the rates at which the limiting values are attained.

Nonstationarities in Hydrologic and Environmental Time Series

Author: A.R. Rao
Publisher: Springer Science & Business Media
ISBN: 9401001170
Format: PDF, ePub, Mobi
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Conventionally, time series have been studied either in the time domain or the frequency domain. The representation of a signal in the time domain is localized in time, i.e . the value of the signal at each instant in time is well defined . However, the time representation of a signal is poorly localized in frequency , i.e. little information about the frequency content of the signal at a certain frequency can be known by looking at the signal in the time domain . On the other hand, the representation of a signal in the frequency domain is well localized in frequency, but is poorly localized in time, and as a consequence it is impossible to tell when certain events occurred in time. In studying stationary or conditionally stationary processes with mixed spectra , the separate use of time domain and frequency domain analyses is sufficient to reveal the structure of the process . Results discussed in the previous chapters suggest that the time series analyzed in this book are conditionally stationary processes with mixed spectra. Additionally, there is some indication of nonstationarity, especially in longer time series.

Time Series Analysis and Cyclostratigraphy

Author: Graham P. Weedon
Publisher: Cambridge University Press
ISBN: 9780521019835
Format: PDF, Mobi
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An essential reference for researchers, and suitable for senior undergraduate and graduate courses in environmental science, palaeoceanography and geology.

Univariate Time Series in Geosciences

Author: Hans Gilgen
Publisher: Springer Science & Business Media
ISBN: 3540309683
Format: PDF, ePub
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This is a detailed introduction to the statistical analysis of geophysical time series, using numerous examples and exercises to build proficiency. The exercises lead the reader to explore the meaning of concepts such as the estimation of the linear time series (AMRA) models or spectra. The book also serves as a guide to using the open-source "R" program for statistical analysis of time series.

Spectral Analysis and Time Series

Author: Maurice Bertram Priestley
Publisher:
ISBN:
Format: PDF, ePub
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A principal feature of this book is the substantial care and attention devoted to explaining the basic ideas of the subject. Whenever a new theoretical concept is introduced it is carefully explained by reference to practical examples drawn mainly from the physical sciences. Subjects covered include: spectral analysis which is closely intertwined with the "time domain" approach, elementary notions of Hilbert Space Theory, basic probability theory, and practical analysis of time series data. The inclusion of material on "kalman filtering", state-space filtering", "non-linear models" and continuous time" models completes the impressive list of unique and detailed features which will give this book a prominent position among related literature. The first section-Volume 1-deals with single (univariate) series, while the second-Volume 2-treats the analysis of several (multivariate) series and the problems of prediction, forecasting and control.

Time Series in Psychology

Author: R. A.M. Gregson
Publisher: Psychology Press
ISBN: 1317769333
Format: PDF, ePub, Mobi
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First published in 1983. Routledge is an imprint of Taylor & Francis, an informa company.

Developments in Time Series Analysis

Author: T. Subba Rao
Publisher: CRC Press
ISBN: 9780412492600
Format: PDF
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This volume contains 27 papers, written by time series analysts, dealing with statistical theory, methodology and applications. The emphasis is on the recent developments in the analysis of linear, onlinear (non-Gaussian), stationary and nonstationary time series. The topics include cointegration, estimation and asymptotic theory, Kalman filtering, nonparametric statistical inference, long memory models, nonlinear models, spectral analysis of stationary and nonstationary processes. Quite a number of papers are devoted to modelling and analysis of real time series, and the econometricians, mathematical statisticians, communications engineers and scientists who use time series techniques and Fourier analysis should find the papers in this volume useful.

A Very British Affair

Author: T. Mills
Publisher: Springer
ISBN: 1137291265
Format: PDF, ePub, Docs
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This book develops the major themes of time series analysis from its formal beginnings in the early part of the 20th century to the present day through the research of six distinguished British statisticians, all of whose work is characterised by the British traits of pragmatism and the desire to solve practical problems of importance.