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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.
11

The transformation of one-dimensional and two-dimensional autoregressive random fields under coordinate scaling and rotation

Kennedy, Ian Douglas January 2008 (has links)
A practical problem in computer graphics is that of representing a textured surface at arbitrary scales. I consider the underlying mathematical problem to be that of interpolating autoregressive random fields under arbitrary coordinate transformations. I examine the theoretical basis for the transformations that autoregressive parameters exhibit when the associated stationary random fields are scaled or rotated. The basic result is that the transform takes place in the continuous autocovariance domain, and that the spectral density and associated autoregressive parameters proceed directly from sampling the continuous autocovariance on a transformed grid. I show some real-world applications of these ideas, and explore how they allow us to interpolate into a random field. Along the way, I develop interesting ways to estimate simultaneous autoregressive parameters, to calculate the distorting effects of linear interpolation algorithms, and to interpolate random fields without altering their statistics.
12

The transformation of one-dimensional and two-dimensional autoregressive random fields under coordinate scaling and rotation

Kennedy, Ian Douglas January 2008 (has links)
A practical problem in computer graphics is that of representing a textured surface at arbitrary scales. I consider the underlying mathematical problem to be that of interpolating autoregressive random fields under arbitrary coordinate transformations. I examine the theoretical basis for the transformations that autoregressive parameters exhibit when the associated stationary random fields are scaled or rotated. The basic result is that the transform takes place in the continuous autocovariance domain, and that the spectral density and associated autoregressive parameters proceed directly from sampling the continuous autocovariance on a transformed grid. I show some real-world applications of these ideas, and explore how they allow us to interpolate into a random field. Along the way, I develop interesting ways to estimate simultaneous autoregressive parameters, to calculate the distorting effects of linear interpolation algorithms, and to interpolate random fields without altering their statistics.
13

Bayesian Techniques for Adaptive Acoustic Surveillance

Morton, Kenneth D. January 2010 (has links)
<p>Automated acoustic sensing systems are required to detect, classify and localize acoustic signals in real-time. Despite the fact that humans are capable of performing acoustic sensing tasks with ease in a variety of situations, the performance of current automated acoustic sensing algorithms is limited by seemingly benign changes in environmental or operating conditions. In this work, a framework for acoustic surveillance that is capable of accounting for changing environmental and operational conditions, is developed and analyzed. The algorithms employed in this work utilize non-stationary and nonparametric Bayesian inference techniques to allow the resulting framework to adapt to varying background signals and allow the system to characterize new signals of interest when additional information is available. The performance of each of the two stages of the framework is compared to existing techniques and superior performance of the proposed methodology is demonstrated. The algorithms developed operate on the time-domain acoustic signals in a nonparametric manner, thus enabling them to operate on other types of time-series data without the need to perform application specific tuning. This is demonstrated in this work as the developed models are successfully applied, without alteration, to landmine signatures resulting from ground penetrating radar data. The nonparametric statistical models developed in this work for the characterization of acoustic signals may ultimately be useful not only in acoustic surveillance but also other topics within acoustic sensing.</p> / Dissertation
14

Nil

Liu, Tse-Tseng 27 July 2000 (has links)
Nil
15

A Nonlinear Mixture Autoregressive Model For Speaker Verification

Srinivasan, Sundararajan 30 April 2011 (has links)
In this work, we apply a nonlinear mixture autoregressive (MixAR) model to supplant the Gaussian mixture model for speaker verification. MixAR is a statistical model that is a probabilistically weighted combination of components, each of which is an autoregressive filter in addition to a mean. The probabilistic mixing and the datadependent weights are responsible for the nonlinear nature of the model. Our experiments with synthetic as well as real speech data from standard speech corpora show that MixAR model outperforms GMM, especially under unseen noisy conditions. Moreover, MixAR did not require delta features and used 2.5x fewer parameters to achieve comparable or better performance as that of GMM using static as well as delta features. Also, MixAR suffered less from overitting issues than GMM when training data was sparse. However, MixAR performance deteriorated more quickly than that of GMM when evaluation data duration was reduced. This could pose limitations on the required minimum amount of evaluation data when using MixAR model for speaker verification.
16

An Application of Multiple Time Series Methods to Canadian Economic Data

Booker, Jill 12 1900 (has links)
This work outlines several aspects of multiple time series analysis, which are then demonstrated on a large set of data. After introducing the general autoregressive integrated moving average model, discussion is restricted to a canonical form: the pure autoregressive process of order p (AR(p)). Methods for identifying and fitting the AR(p) process using quasi-partial correlation matrices and Akaike's AIC criterion are discussed. The AR model can then be used to make forecasts by taking conditional expectations at the origin time. Probability limits on the forecasts are also defined. A method for canonical analysis of AR processes is described which can indicate possible reductions in the dimensionality of the problem. Using computer programs developed for this project, the above methods are applied to an 11-dimensional set of Canadian economic data and the results are discussed. / Thesis / Master of Science (MSc)
17

Inflation Targeting in Developing Countries and Its Applicability to the Turkish Economy

Tutar, Eser 01 August 2002 (has links)
Inflation targeting is a monetary policy regime, characterized by public announcement of official target ranges or quantitative targets for price level increases and by explicit acknowledgement that low inflation is the most crucial long-run objective of the monetary authorities. There are three prerequisites for inflation targeting: 1)central bank independence,2)having a sole target,3)existence of stable and predictable relationship between monetary policy instruments and inflation.In many developing countries, the use of seigniorage revenues as an important source of financing public debts, the lack of commitment to low inflation as a primary goal by monetary authorities, considerable exchange rate flexibility, lack of substantial operational independence of the central bank or of powerful models to make domestic inflation forecasts hinder the satisfaction of these requirements. This study investigates the applicability of inflation targeting to the Turkish economy. Central bank independence in Turkey has been mainly hindered by "fiscal dominance" through monetization of high budget deficits. In addition, although serious steps have been taken recently under a new law to have an independent central bank, such as formal commitment to the achievement of price stability as the primary objective and the prohibition of credit extension to the government, the central bank does not satisfy independence criteria due to the problems associated with the appointment of the government and the share of the Treasury within the bank. Having a sole inflation target was hindered by the existence of fixed exchange rate system throughout the years. However, in February 2001, Turkey switched to a floating exchange rate regime, which is important for a successful inflation-targeting regime. Having a sole target within the system has also been supported by the new central bank law, which gives priority to price stability and supports any other objective as long as it is consistent with price stability. In this thesis, an empirical investigation has been made in order to assess the statistical readiness of Turkey to satisfy the requirements of inflation-targeting by making use of vector autoregressive (VAR) models. The results suggest that inflation is an inertial phenomenon in Turkey and money, interest rates and nominal exchange rates innovations are not economically and statistically important determinants of prices. Most of the variances in prices are explained by prices themselves. According to the VAR evidence, the direct linkages between monetary policy instruments and inflation do not seem to be strong, stable, and predictable. As a result, while the second requirement of the inflation-targeting regime seems to have been satisfied, there are still problems associated with the central bank independence and the existence of stable and predictable relationship between monetary policy instruments and inflation in Turkey. / Master of Arts
18

An Application of Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Modelling on Taiwan's Time-Series Data: Three Essays

Chang, Tsangyao 01 May 1995 (has links)
In this dissertation, three essays are presented that apply recent advances in time-series methods to the analysis of inflation and stock market index data for Taiwan. Specifically, ARCH and GARCH methodologies are used to investigate claims of increased volatility in economic time-series data since 1980. In the first essay, analysis that accounts for structural change reveals that the fundamental relationship between inflation and its variability was severed by policies implemented during economic liberalization in Taiwan in the early 1980s. Furthermore, if residuals are corrected for serial correlation, evidence in favor of ARCH effects is weakened. In the second essay, dynamic linkages between daily stock returns and daily trading volume are explored. Both linear and nonlinear dependence are evaluated using Granger causality tests and GARCH modelling. Results suggest significant unidirectional Granger causality from stock returns to trading volume. In the third essay, comparative analysis of the frequency structure of the Taiwan stock index data is conducted using daily, weekly, and monthly data. Results demonstrate that the relationship between mean return and its conditional standard deviation is positive and significant only for high-frequency daily data.
19

An Application of Autoregressive Conditional Heteroskedasticity (Arch) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Modelling on Taiwan's Time-Series Data: Three Essays

Chang, Tsangyao 01 January 1995 (has links)
In this dissertation, three essays are presented that apply recent advances in time-series methods to the analysis of inflation and stock market index data for Taiwan. Specifically, ARCH and GARCH methodologies are used to investigate claims of increased volatility in economic time-series data since 1980. In the first essay, analysis that accounts for structural change reveals that the fundamental relationship between inflation and its variability was severed by policies implemented during economic liberalization in Taiwan in the early 1980s. Furthermore, if residuals are corrected for serial correlation, evidence in favor of ARCH effects is weakened. In the second essay, dynamic linkages between daily stock returns and daily trading volume are explored. Both linear and nonlinear dependence are evaluated using Granger causality tests and GARCH modelling. Results suggest significant unidirectional Granger causality from stock returns to trading volume. In the third essay, comparative analysis of the frequency structure of the Taiwan stock index data is conducted using daily, weekly, and monthly data. Results demonstrate that the relationship between mean return and its conditional standard deviation is positive and significant only for high-frequency daily data.
20

Essays on oil price shocks and financial markets

Wang, Jiayue January 2012 (has links)
This thesis is composed of three chapters, which can be read independently. The first chapter investigates how oil price volatility affects the investment decisions for a panel of Japanese firms. The model is estimated using a system generalized method of moments technique for panel data. The results are presented to show that there is a U-shaped relationship between oil price volatility and Japanese firm investment. The results from subsamples of these data indicate that this U-shaped relationship is more significant for oil-intensive firms and small firms. The second chapter aims to examine the underlying causes of changes in real oil price and their transmission mechanisms in the Japanese stock market. I decompose real oil price changes into three components; namely, oil supply shock, aggregate demand shock and oil-specific demand shock, and then estimate the dynamic effects of each component on stock returns using a structural vector autoregressive (SVAR) model. I find that the responses of aggregate Japanese real stock returns differ substantially with different underlying causes of oil price changes. In the long run, oil shocks account for 43% of the variation in the Japanese real stock returns. The response of Japanese real stock returns to oil price shocks can be attributed in its entirety to the cash flow variations. The third chapter tests the robustness of SVAR and investigates the impact of oil price shocks on the different U.S. stock indices. I find that the responses of real stock returns of alternate stock indices differ substantially depending on the underlying causes of the oil price increase. However, the magnitude and length of the effect depends on the firm size. The response of U.S. stock returns to oil price shocks can be attributed to the variations of expected discount rates and expected cash flows.

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