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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

One-dependence and k-block factors

Goulet, Marc 21 February 1992 (has links)
Graduation date: 1993
2

One-dependence and k-block factors /

Goulet, Marc. January 1992 (has links)
Thesis (Ph. D.)--Oregon State University, 1993. / Typescript (photocopy). Includes bibliographical references (leaves 35-36). Also available on the World Wide Web.
3

On the limit distributions of high level crossings by a stationary process

Bélisle, Claude January 1981 (has links)
No description available.
4

On the limit distributions of high level crossings by a stationary process

Bélisle, Claude January 1981 (has links)
No description available.
5

Noncommutative stationary processes /

Gohm, Rolf. January 2004 (has links)
Univ., Habil.-Schr. u.d.T.: Gohm, Rolf: Elements of a spatial theory for noncommutative stationary processes with discrete time index--Greifswald, 2002. / Literaturverz. S. [165] - 168.
6

Nestacionární časové řady / Non-stationary time series

Večeřa, Jakub January 2014 (has links)
This thesis focuses on option of omitting the stationarity assumption, which is usually used in the financial time series analysis. The theory of semi-stationary processes is introduced. This type of process has time-dependent spectra (the evolutionary spectra) in comparison with stationary process. The evolutionary spectra estimator is derived using a linear filter and then averaged in time to reduce any fluctuations caused by randomness. Predictions and variance estimates are retrieved from the estimated time dependent spectra. The semi-stationary processes theory is applied to the ARMA processes with time-dependent coefficients, a coefficient estimator based on evolutionary spectra is suggested. Calculations are performed in R software. Powered by TCPDF (www.tcpdf.org)
7

Estimating partial group delay

Zhang, Nien-fan January 1985 (has links)
Partial group delay is a spectral parameter, which measures the time lag between two time series in a system after the spurious effects of the other series in the system have been eliminated. For weakly-stationary processes, estimators for partial group delay are proposed based on indirect and direct approaches. Conditions for weak consistency and asymptotic normality of the proposed estimators are obtained. Applications to a multiple test of partial group delay are investigated. The time lag interpretation of partial group delay is justified, which provides insight into the nature of linear relationships among weakly-stationary processes. Extensions are made to group delay estimation and partial group delay estimation for non-stationary "oscillatory" processes. / Ph. D.
8

Envelopes of broad band processes

Van Dyke, Jozef Frans Maria January 1981 (has links)
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Civil Engineering, 1981. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Bibliography: leaf 93. / by Jozef Frans Maria Van Dyke. / M.S.
9

Maximally smooth transition: the Gluskabi raccordation

Yeung, Deryck 24 August 2011 (has links)
The objective of this dissertation is to provide a framework for constructing a transitional behavior, connecting any two trajectories from a set with a particular characteristic, in such a way that the transition is as inconspicuous as possible. By this we mean that the connection is such that the characteristic behavior persists during the transition. These special classes include stationary solutions, limit cycles etc. We call this framework the Gluskabi raccordation. This problem is motivated from physical applications where it is often desired to steer a system from one stationary solution or periodic orbit to another in a ̒smooth̕ way. Examples include motion control in robotics, chemical process control and quasi-stationary processes in thermodynamics, etc. Before discussing the Gluskabi raccordations of periodic behaviors, we first study several periodic phenomena. Specifically, we study the self- propulsion of a number of legless, toy creatures based on differential friction under periodic excitations. This friction model is based on viscous friction which is predominant in a wet environment. We investigate the effects of periodic and optimal periodic control on locomotion. Subsequently, we consider a control problem of a stochastic system, under the basic constraint that the feedback control signal and the observations from the system cannot use the communication channel simultaneously. Hence, two modes of operation result: an observation mode and a control mode. We seek an optimal periodic regime in a statistical steady state by switching between the observation and the control mode. For this, the duty cycle and the optimal gains for the controller and observer in either mode are determined. We then investigate the simplest special case of the Gluskabi raccordation, namely the quasi-stationary optimal control problem. This forces us to revisit the classical terminal controller. We analyze the performance index as the control horizon increases to infinity. This problem gives a good example where the limiting operation and integration do not commute. Such a misinterpretation can lead to an apparent paradox. We use symmetrical components (the parity operator) to shed light on the correct solution. The main part of thesis is the Gluskabi raccordation problem. We first use several simple examples to introduce the general framework. We then consider the signal Gluskabi raccordation or the Gluskabi raccordation without a dynamical system. Specifically, we present the quasi-periodic raccordation where we seek the maximally ̒smooth̕ transitions between two periodic signals. We provide two methods, the direct and indirect method, to construct these transitions. Detailed algorithms for generating the raccordations based on the direct method are also provided. Next, we extend the signal Gluskabi raccordation to the dynamic case by considering the dynamical system as a hard constraint. The behavioral modeling of dynamical system pioneered by Willems provides the right language for this generalization. All algorithms of the signal Gluskabi raccordation are extended accordingly to produce these ̒smooth̕ transition behaviors.
10

Directed wavelet covariance for locally stationary processes / Covariância direcionada de ondaletas para processos localmente estacionários

Lopes, Kim Samejima Mascarenhas 12 March 2018 (has links)
The main goal of this study is to propose a methodology that measures directed relations between locally stationary processes. Unlike stationary processes, locally stationary processes may present sudden pattern changes and have local characteristics in specific intervals. This behavior causes instability in measures based on Fourier transforms. The relevance of this study relies on considering these processes and propose robust methodologies that are not affected by outliers, sudden pattern changes or local behavior. We start reviewing the Partial Directed Coherence (PDC) and the Wavelet Coherence. PDC measures the directed relation between components of a multivariate stationary Vector Autoregressive (VAR) model in the frequency domain, while Wavelet Coherence is based on complex wavelets decomposition. We then propose a causal wavelet decomposition of the covariance structure for bivariate locally stationary processes: the Directed Wavelet Covariance (DWC). Compared to Fourier-based quantities, wavelet-based estimators are more appropriate for non-stationary processes and processes with local patterns, outliers and rapid regime changes like in EEG experiments with the introduction of stimuli. We then propose its estimators and calculate its expectation and analyze its variance. Next we propose a decomposition for the variance of multivariate processes with more than two components: the Partial Directed Wavelet Covariance (pDWC). Considering a N-variate locally stationary process, the pDWC calculates the Directed Wavelet Covariance of X_1(t) with X_2(t) eliminating the effect of the other components X_3(t), ... ,X_N(t). We propose two approaches to this situation. First we filter the multivariate process to remove all the exogenous influences and then we calculate the directed relation between the components. In the second case, as in Partial Directed Coherence, we consider the multivariate process as a time-varying Vector Autoregressive Model (tv-VAR) and use its coefficients in the decomposition of the covariance function to isolate the effects of the other components. We also compare results of the PDC, Wavelet Coherence and Directed Wavelet Covariance with simulated data. Finally, we present an application of the proposed Directed Wavelet Covariance and Partial Directed Wavelet Covariance on EEG data. Simulation results show that the proposed measures capture the simulated relations. The pDWC with linear filter has shown more stable estimations than the proposed pDWC considering the tv-VAR. Future studies will discuss the DWC\'s and pDWC\'s asymptotic distributions and significance tests. The proposed Directed Wavelet Covariance decomposition is a different approach to deal with non-stationary processes in the context of causality. The use of wavelets is a gain and adds to the number of studies that can be addressed when Fourier transform does not apply. The pDWC is an alternative for multivariate processes and it removes linear influences from observed external components. / O objetivo deste trabalho é propor uma metodologia para mensurar o impacto direcionado entre processos localmente estacionários. Diferente de processos estacionários, processos localmente estacionários podem apresentar mudanças bruscas e características específicas em determinados intervalos. Tal comportamento pode causar instabilidade em medidas baseadas na transformada de Fourier. A importância deste estudo se dá em englobar processos com tais características, propondo metodologias robustas que não são afetadas pela existência de mudanças bruscas, pontos discrepantes e comportamentos locais. Inicialmente apresentamos conceitos já existentes na literatura, como a Coerência Parcial Direcionada (PDC) e a Coerência de Ondaletas. A PDC mede o impacto direcionado entre componentes de um modelo vetorial autoregressivo (VAR) no domínio da frequência. A coerência de ondaletas é baseada em transformadas complexas de ondaletas. Propomos então uma decomposição no domínio de ondaletas para a estrutura de covariância de processos bivariados localmente estacionários: a Covariância Direcionada de Ondaletas (DWC). Em comparação com as quantidades baseadas na tranformada Fourier, os estimadores baseados em ondaletas são mais apropriados para processos não estacionários com padrões locais, pontos discrepantes ou mudanças rápidas de regime, como em experimentos de eletroencefalograma (EEG) com a introdução de estímulo. Ainda, propomos um estimador para a DWC, calculamos a esperança deste estimador e avaliamos sua variância. Em seguida, propomos uma quantidade análoga à DWC para processos multivariados com mais de duas componentes: a Covariância Parcial Direcionada de Ondaletas (pDWC). Considerando um processo N-variado localmente estacionário, a pDWC calcula a Covariância Direcionada de Ondaletas entre X_1(t) e X_2(t) eliminando o efeito das outras componentes X_3(t), ... , X_N(t). Propomos duas abordagens para a pDWC: na primeira, a pDWC é calculada após a aplicação de um filtro linear que remove o efeito das variáveis exógenas. No segundo caso, a exemplo da Coerência Parcial Direcionada, consideramos o processo multivariado como um Modelo Autoregressivo de Vetorial variante no tempo (tv-VAR) e usamos seus coeficientes na decomposição da função de covariância para isolar os efeitos das demais componentes. Também comparamos os resultados da PDC, Coerência de Ondaletas e Covariância Direcionada de Ondaletas com dados simulados. Por fim, apresentamos uma aplicação da DWC e da pDWC em dados de EEG. Identificamos nas simulações que tanto as medidas já existentes na literatura quanto as quantidades propostas identificaram as relações simuladas. A pDWC proposta com filtros lineares apresentou estimações mais estáveis do que a pDWC considerando os modelos tv-VAR. Estudos futuros discutirão as propriedades assintóticas e testes de significância da DWC e pDWC.

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