Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 34 for “"Covariance Function"”.
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Non-Parametric Spatial Models
<p>Covariance functions play a central role in spatial statistics. Parametric covariance functions have been used in most of the existing works on the analysis of spatial data. The primary reason for this is that the classes of parametric covariance functions guarantee that the fitted covariance …
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Transforms for prediction residuals based on prediction inaccuracy modeling
… as the energy compaction property. Given the covariance function of the signal, the linear transform with the best energy compaction property is the Karhunen Loeve transform. In this thesis, we develop a new set of transforms for prediction residuals. We observe that the prediction process in …
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Performance analysis of broadband multimedia wireless communication networks
… outline the analytical derivation for obtaining covariance function of number of real-time and non-real-time arrivals to a particular downstream link through the switch. We match the covariance function values at different lags with the covariance function of 2-state MMPP at corresponding lags in …
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Spatial marked point processes: Models and inferences
… marked point processes. First, we derive a covariance function of additive models for marked point processes. This covariance function carries information of dependence between points and marks, which can be used in kriging to make predictions of marks at unknown locations. We expect to …
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Propagation of acceleration waves in random media
… White Noise, (ii) Gaussian with a Cauchy covariance function, (iii) Gaussian with a Dagum covariance function. Various coupling scenarios of the material coefficients are considered: (i) positive correlation, (ii) negative correlation, (iii) zero correlation. Similarly to the deterministic …
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The best truncation point for the estimated spectral density function of a stationary time series
… the scientist estimates the spectral density function of the process. One type of spectral density estimator is obtained by using the periodogram representation of the spectral density function. However, if the estimator is to be consistent, a weight function which satisfies certain conditions …
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Particle Spreading in a Simple Majda Flow and Eigenvalue Estimation Through a Cayley Transform
… of the integral operator whose kernel is the covariance function of a random variable plays an important role in finding the distribution of the random variable. We consider methods for producing estimates of the eigenvalues of such operators. If such an operator can be written in the form H0 …
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Clustering Methods for Delineating Regions of Spatial Stationarity
… further investigate data extracted by the use of Functional Magnetic Resonance Imaging (FMRI) as it is applied to brain tissue and how it measures blood flow to certain areas of the brain following the application of a stimulus. As a precursor to detailed spatial analysis of this kind of data, …
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TESTING THE EQUALITY OF SEVERAL COVARIANCE FUNCTIONS FOR FUNCTIONAL DATA
In functional data analysis, one-way ANOVA problems have been studied by many researchers in recent decades. And the equal-covariance assumption is commonly assumed in these equal-mean function testing problems. So it is of interest to check whether this assumption holds or not. In this thesis, we …
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On Self-Similar Gaussian Processes
… formula for a class of Gaussian processes with a covariance function satisfying minimal regularity and integrability conditions. The existence of the local time and a version of Tanaka's formula are derived. These results are applied to a general class of self-similar processes that includes the …
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Uncertainty quantification and calibration in nuclear safety codes using Gaussian process active learning
… when dealing with complex and highly non-linear functions. Methods have been developed to decrease the computational burden by using the Gaussian Process (GP) emulator model framework to approximate the input-output relation of a deterministic computer code. The GP emulator can then be used in …
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Parametric Estimation of the Heston Model under the Indirect Observability Framework
… the $L^2$ convergence between the empirical covariance function (based on the observed data which are sampled from the approximating process) and the actual covariance function of the limiting process. Hence, the consistent and robust parameter estimators based on the Method of Moments are …
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Probabilistic Models on Fibre Bundles
… this diffusion kernel gives rise to a natural covariance function when defining Gaussian processes (GP) on the fibre bundle. To demonstrate the uses of GP on a fibre bundle, we apply it to simulated data on a Mobius strip for the problem of prediction and regression. Parameter tuning can also …
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Gaussian process models for SCADA data based wind turbine performance/condition monitoring
… and nonparametric model whose distribution function is the joint distribution of a collection of random variables; it is widely suitable for classification and regression problems. GP is a machine learning algorithm that uses a measure of similarity between subsequent data points (via …
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Stochastic methods for uncertainty quantification in radiation transport
… a second-order random process with known covariance function in terms of a set of uncorrelated random variables and the eigenmodes of the covariance function. The flux and, in multiplying materials, the k-eigenvalue, which are the problem unknowns, are always expanded in a gPC expansion …
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The effect of locational uncertainty in geostatistics
… of locational error on the spatial lag, the covariance function, the variogram, and optimal spatial prediction (aka, kriging). We show that the basic methodology of kriging adjusted for locational error is the same as kriging without locational error;We also develop a hierarchical Bayesian …
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A beam pattern design procedure for multidimensional sonar arrays employing minimum variance beamforming
… is implemented by generating a "penalty function" in a spectral covariance function form. Processing the penalty function causes beam pattern high sidelobes to be penalized and the main lobe to be emphasized. This is accomplished by forming the penalty function in terms of an isotropic …
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Three essays in econometrics
… structure is not accurately captured by the auto-covariance function. I study the statistical properties of quantile spectral estimators in a large class of nonlinear time series models and discuss inference both at fixed and across all frequencies. Monte Carlo experiments and an empirical example …
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Estimation and Testing Methods for Monotone Transformation Models
… normal with mean zero (unbiased) and variance-covariance matrix consistently estimated by the usual sandwich-type estimator. An iterative algorithm is given for the variance estimation and shown to numerically converge to a consistent limiting variance estimator. The self-induced smoothing …
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