Nonparametric Spectrum Estimation for Spatial Data
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Total Pages | : |
Release | : 2006 |
Genre | : Parameter estimation |
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Release | : 2006 |
Genre | : Parameter estimation |
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Total Pages | : 22 |
Release | : 2008 |
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Smoothed nonparametric kernel spectral density estimates are considered for stationary data observed on a d-dimensional lattice. The implications for edge effect bias of the choice of kernel and bandwidth are considered. Under some circumstances the bias can be dominated by the edge effect. We show that this problem can be mitigated by tapering. Some extensions and related issues are discussed. MSC: 62M30, 62M15 C22.
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Release | : 2000 |
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The empirical semivariogram of residuals from a regression model withstationary errors may be used to estimate the covariance structure of the underlyingprocess. For prediction (Kriging) the bias of the semivariogram estimate induced byusing residuals instead of errors has only a minor effect because thebias is small for small lags. However, for estimating the variance of estimatedregression coefficients and of predictions, the bias due to using residuals can be quite substantial. Thus wepropose a method for reducing the bias in empirical semivariogram estimatesbased on residuals. The adjusted empirical semivariogram is then isotonizedand made positive definite and used to estimate the variance of estimatedregression coefficients in a general estimating equations setup. Simulation results for least squares and robust regression show that theproposed method works well in linear models withstationary correlated errors. Spectral Analysis with Spatial Periodogram and Data Tapers.(Under the direction of Professor Montserrat Fuentes.)The spatial periodogram is a nonparametric estimate of the spectral density, which is the Fourier Transform of the covariance function. The periodogram is a useful tool to explain the dependence structure of aspatial process. Tapering (data filtering) is an effective technique to remove the edge effects even inhigh dimensional problemsand can be applied to the spatial data in order to reduce the bias of the periodogram. However, the variance of the periodogram increases as the bias is reduced. We present a method to choose an appropriate smoothing parameter for datatapers and obtain better estimates of the spectral densityby improving the properties of the periodogram. The smoothing parameter is selected taking intoaccount the trade-off between bias and variance of the taperedperiodogram. We introduce a new asymptotic approach for spatial datacalled `shrinking asymptotics', which combines theincreasing-domain and the fixed-domain asymptotics. With th.
Author | : Rosa María Crujeiras Casais |
Publisher | : Univ Santiago de Compostela |
Total Pages | : 261 |
Release | : 2007 |
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Author | : Francis Castanié |
Publisher | : John Wiley & Sons |
Total Pages | : 297 |
Release | : 2013-02-04 |
Genre | : Mathematics |
ISBN | : 1118601831 |
Digital Spectral Analysis provides a single source that offers complete coverage of the spectral analysis domain. This self-contained work includes details on advanced topics that are usually presented in scattered sources throughout the literature. The theoretical principles necessary for the understanding of spectral analysis are discussed in the first four chapters: fundamentals, digital signal processing, estimation in spectral analysis, and time-series models. An entire chapter is devoted to the non-parametric methods most widely used in industry. High resolution methods are detailed in a further four chapters: spectral analysis by stationary time series modeling, minimum variance, and subspace-based estimators. Finally, advanced concepts are the core of the last four chapters: spectral analysis of non-stationary random signals, space time adaptive processing: irregularly sampled data processing, particle filtering and tracking of varying sinusoids. Suitable for students, engineers working in industry, and academics at any level, this book provides a rare complete overview of the spectral analysis domain.
Author | : Hyon-Jung Kim |
Publisher | : |
Total Pages | : 97 |
Release | : 2000 |
Genre | : |
ISBN | : |
Keywords: Estimating equations, Matern Family, REML, Sandwich variance estimator, Spatial periodogram, Spectral density, Data tapers, Smoothing parameter, Shrinking asymptotics.
Author | : Petre Stoica |
Publisher | : Prentice Hall |
Total Pages | : 492 |
Release | : 2005 |
Genre | : Mathematics |
ISBN | : |
Designed for introductory courses on Spectral Analysis at the graduate or advanced undergraduate level for students, researchers, and practitioners in the area of Signal Processing, this text is an expanded edition of "Introduction to Spectral Analysis". It includes nonparametric spectrum analysis, parametric spectral analysis and parametric met.
Author | : Douglas D. Walker |
Publisher | : |
Total Pages | : 366 |
Release | : 1994 |
Genre | : Geology |
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Author | : Aspasia Zerva |
Publisher | : CRC Press |
Total Pages | : 488 |
Release | : 2016-04-19 |
Genre | : Science |
ISBN | : 1420009915 |
The spatial variation of seismic ground motions denotes the differences in the seismic time histories at various locations on the ground surface. This text focuses on the spatial variability of the motions that is caused by the propagation of the waveforms from the earthquake source through the earth strata to the ground surface, and it brings toge
Author | : Jeffery Alan Sullivan |
Publisher | : |
Total Pages | : 368 |
Release | : 1984 |
Genre | : Earth sciences |
ISBN | : |