An Automatic Portmanteau Test For Nonlinear Dependence

A data-driven version of a portmanteau test for detecting nonlinear types of statistical dependence is considered. An attractive feature of the proposed test is that it properly controls type I error without depending on the number of lags. In addition, the automatic test is found to have higher power in simulations when compared to the McLeod and Li test, for both raw data and residuals

A Genetic Algorithm Approach for Prediction of Linear Dynamical Systems

Modelling of linear dynamical systems is very important issue in science and engineering. The modelling process might be achieved by either the application of the governing laws describing the process or by using the input-output data sequence of the process. Most of the modelling algorithms reported in the literature focus on either determining the order or estimating the model parameters. In this paper, the authors present a new method for modelling. Given the input-output data sequence of the model in the absence of any information about the order, the correct order of the model as well as the correct parameters is determined simultaneously using genetic algorithm. The algorithm used in this paper has several advantages; first, it does not use complex mathematical procedures in detecting the order and the parameters; second, it can be used for low as well as high order systems; third, it can be applied to any linear dynamical system including the autoregressive, moving-average, and autoregressive moving-average models; fourth, it determines the order and the parameters in a simultaneous manner with a very high accuracy. Results presented in this paper show the potentiality, the generality, and the superiority of our method as compared with other well-known methods

Adequacy assessment of composite generation and transmission systems incorporating wind energy conversion systems

The development and utilization of wind energy for satisfying electrical demand has received considerable attention in recent years due to its tremendous environmental, social and economic benefits, together with public support and government incentives. Electric power generation from wind energy behaves quite differently from that of conventional sources. The fundamentally different operating characteristics of wind energy facilities therefore affect power system reliability in a different manner than those of conventional systems. The reliability impact of such a highly variable energy source is an important aspect that must be assessed when the wind power penetration is significant. The focus of the research described in this thesis is on the utilization of state sampling Monte Carlo simulation in wind integrated bulk electric system reliability analysis and the application of these concepts in system planning and decision making. Load forecast uncertainty is an important factor in long range planning and system development. This thesis describes two approximate approaches developed to reduce the number of steps in a load duration curve which includes load forecast uncertainty, and to provide reasonably accurate generating and bulk system reliability index predictions. The developed approaches are illustrated by application to two composite test systems. A method of generating correlated random numbers with uniform distributions and a specified correlation coefficient in the state sampling method is proposed and used to conduct adequacy assessment in generating systems and in bulk electric systems containing correlated wind farms in this thesis. The studies described show that it is possible to use the state sampling Monte Carlo simulation technique to quantitatively assess the reliability implications associated with adding wind power to a composite generation and transmission system including the effects of multiple correlated wind sites. This is an important development as it permits correlated wind farms to be incorporated in large practical system studies without requiring excessive increases in computer solution time. The procedures described in this thesis for creating monthly and seasonal wind farm models should prove useful in situations where time period models are required to incorporate scheduled maintenance of generation and transmission facilities. There is growing interest in combining deterministic considerations with probabilistic assessment in order to evaluate the quantitative system risk and conduct bulk power system planning. A relatively new approach that incorporates deterministic and probabilistic considerations in a single risk assessment framework has been designated as the joint deterministic-probabilistic approach. The research work described in this thesis illustrates that the joint deterministic-probabilistic approach can be effectively used to integrate wind power in bulk electric system planning. The studies described in this thesis show that the application of the joint deterministic-probabilistic method provides more stringent results for a system with wind power than the traditional deterministic N-1 method because the joint deterministic-probabilistic technique is driven by the deterministic N-1 criterion with an added probabilistic perspective which recognizes the power output characteristics of a wind turbine generator

Essays on diagnostic testing in time series model

The Ph.D thesis, titled Essays On Diagnostic Testing In Time Series Models, investigates several issues related to inference in time series models. The aim is to develop a deeper understanding of issues involving hypothesis testing and inference in models that exhibit some non-linear dependence or time-varying endogeneity. This thesis is made up of five main chapters, In the first chapter (Chapter 1) we provide a motivation for the thesis. In the second chapter (Chapter 2), we develop a data-driven version of a portmanteau test for detecting nonlinear types of statistical dependence. The test properly controls the type I error without being sensitive with respect to the number of autocorrelations used. In addition, the automatic test is found to have higher power in simulations when compared to the standard portmanteau test, for both raw data and residuals. In the third chapter (Chapter 3), we propose a bootstrap version of a time-varying Hausman test statistic, which compares kernel based time-varying OLS and IV estimators of regression coefficients, allowing for possible changes in the endogeneity status of the regressors over time. In this chapter, we examine the finite-sample performance of the asymptotic and the bootstrap version of the test by means of Monte Carlo simulations and we establish the asymptotic validity of a simple, easy to use bootstrap procedure. The bootstrap test has more accurate size and higher power than its asymptotic counterpart. What is more, it is demonstrated that the size and power of the bootstrap test are insensitive with respect to the choice of the bandwidth parameters. This is of particular importance since in current practice researchers use a variety of ad hoc approaches to bandwidth selection which are typically based on objective functions that address estimation concerns rather than test accuracy. In the fourth chapter (Chapter 4), we study the problem of bandwidth choice for non-parametric instrumental variable and least square estimation for econometric models whose coefficients can vary over time either de-terministically or stochastically, under both endogeneity and exogeneity. In this chapter, we compare different data-driven selectors for the smoothing parameter. We find that data-driven methods perform well for both the estimators. Quite interestingly, we find that selecting the bandwidth parameter in a data-driven way for the time-varying least square estimation under endogeneity provides a way to reduce the finite small sample bias of the estimator. In the last chapter we summarize the results of the thesis

Bulk electric system reliability simulation and application

Bulk electric system reliability analysis is an important activity in both vertically integrated and unbundled electric power utilities. Competition and uncertainty in the new deregulated electric utility industry are serious concerns. New planning criteria with broader engineering consideration of transmission access and consistent risk assessment must be explicitly addressed. Modern developments in high speed computation facilities now permit the realistic utilization of sequential Monte Carlo simulation technique in practical bulk electric system reliability assessment resulting in a more complete understanding of bulk electric system risks and associated uncertainties. Two significant advantages when utilizing sequential simulation are the ability to obtain accurate frequency and duration indices, and the opportunity to synthesize reliability index probability distributions which describe the annual index variability. This research work introduces the concept of applying reliability index probability distributions to assess bulk electric system risk. Bulk electric system reliability performance index probability distributions are used as integral elements in a performance based regulation (PBR) mechanism. An appreciation of the annual variability of the reliability performance indices can assist power engineers and risk managers to manage and control future potential risks under a PBR reward/penalty structure. There is growing interest in combining deterministic considerations with probabilistic assessment in order to evaluate the “system well-being” of bulk electric systems and to evaluate the likelihood, not only of entering a complete failure state, but also the likelihood of being very close to trouble. The system well-being concept presented in this thesis is a probabilistic framework that incorporates the accepted deterministic N-1 security criterion, and provides valuable information on what the degree of the system vulnerability might be under a particular system condition using a quantitative interpretation of the degree of system security and insecurity. An overall reliability analysis framework considering both adequacy and security perspectives is proposed using system well-being analysis and traditional adequacy assessment. The system planning process using combined adequacy and security considerations offers an additional reliability-based dimension. Sequential Monte Carlo simulation is also ideally suited to the analysis of intermittent generating resources such as wind energy conversion systems (WECS) as its framework can incorporate the chronological characteristics of wind. The reliability impacts of wind power in a bulk electric system are examined in this thesis. Transmission reinforcement planning associated with large-scale WECS and the utilization of reliability cost/worth analysis in the examination of reinforcement alternatives are also illustrated

Bootstrap für autoregressive moving average Prozesse

The main focus of this thesis is to develop bootstrap approaches for the class of continuous-time autoregressive moving average processes. The first part focuses on the classical setup for time series in discrete time, especially the class of linear processes is considered in detail. Central limit results for integrated periodograms and ratio statistics are shown to change essentially when the estimates are based on low-frequency observations instead of full-time observations. The results correspond to those for continuous-time parameters based on discrete observations. This motivates the further parts of this thesis. The second part proposes bootstrap possibilities for continuous-time autoregressive processes. Samples of such processes have representations as autoregressions but with uncorrelated innovation sequence only. To circumvent the interdependencies in the innovations, the underlying continuous-time model is used. This allows for a parametric representation with truely independent innovations which is favorable for the bootstrap, however, there is a certain price to pay. Nevertheless, on this basis a valid residual-based bootstrap approach is developed. The third part considers the bootstrap for continuous-time autoregressive moving average processes. As for pure continuous-time autoregressions, the samples fulfill an autoregessive representation with uncorrelated innovations only. Due to the moving average part, residual-based proposals are not adaptable here. However, the block bootstrap is valid, since it works under very mild assumptions. The additional information on the autoregressive part of the process motivates a two-step bootstrap approach. First, an autoregressive model is fitted. In a second step - to address the remaining dependencies - the block bootstrap is applied. This approach is shown to be valid. Its applicability is tailor-made but not limited to continuous-time autoregressive moving averages. Indeed, it is as widely applicable as standard block bootstraps and thus suitably generalizes the moving block bootstrap. The approach further robustifies the residual bootstrap.Der Schwerpunkt dieser Arbeit liegt auf der Entwicklung von Bootstrap-Ansätzen für die Klasse der zeitstetigen autoregressiven moving average Prozesse. Der erste Teil konzentriert sich auf klassische Zeitreihen in diskreter Zeit und dabei insbesondere auf lineare Prozesse. Zentrale Grenzwertresultate für integrierte Periodogramme und sogenannte Ratio Statistics werden unter der Annahme von Beobachtungen auf niederen Frequenzen entwickelt. Wesentliche Unterschiede zur Situation mit vollständigen Beobachtungen werden aufgezeigt. Die Ergebnisse korrespondieren zu denen für zeitstetige Parameter, die mithilfe von diskreten Beobachtungen geschätzt werden. Diese Eigenschaft motiviert die weiteren Teile dieser Arbeit. Der zweite Teil stellt Bootstrap-Möglichkeiten für zeitstetige autoregressive Prozesse vor. Beobachtungen solcher Prozesse besitzen eine autoregressive Darstellung, jedoch nur mit unkorrelierten Innovationen. Um die Problematik der Abhängigkeiten innerhalb der Innovationen zu lösen, wird das zugrundeliegende zeitstetige Modell benutzt. Dieses erlaubt eine parametrische Darstellung mit unabhängigen Innovationen, die für Bootstrap-Ansätze günstig ist. Für diese Darstellung ist jedoch ein gewisser Preis zu zahlen. Dennoch kann ein valider Residuen-basierter Bootstrap-Ansatz entwickelt werden. Der dritte Teil behandelt Bootstrap-Ansätze für zeitstetige autoregressive moving average Prozesse. Wie für stetige autoregressive Prozesse erfüllen solche Stichproben eine autoregressive Darstellung mit nur unkorrelierten Innovationen. Aufgrund des moving average-Anteils lassen sich keine Residuen-basierten Ansätze adaptieren. Die zusätzliche Information über die autoregressive Prozessdarstellung motivert einen zweistufigen Bootstrap-Ansatz. Zunächst wird ein autoregressives Modell angepasst. Als zweites wird der allgemeine Block-Bootstrap angewendet, um die verbliebenen Abhängigkeiten zu adressieren. Die Validität des Ansatzes wird bewiesen. Der Ansatz ist maßgeschneidert, aber nicht beschränkt auf die zeitstetigen autoregressiven moving average Prozesse. Genauer ist die Methode genauso allgemein anwendbar wie der Standard-Block-Bootstrap. Der Ansatz erweitert den Moving-Block-Bootstrap sinnvoll und robustifiziert außerdem den Residuen-basierten Bootstrap

Glottal source parametrisation by multi-estimate fusion

Glottal source information has been proven useful in many applications such as speech synthesis, speaker characterisation, voice transformation and pathological speech diagnosis. However, currently no single algorithm can extract reliable glottal source estimates across a wide range of speech signals. This thesis describes an investigation into glottal source parametrisation, including studies, proposals and evaluations on glottal waveform extraction, glottal source modelling by Liljencrants-Fant (LF) model fitting and a new multi-estimate fusion framework. As one of the critical steps in voice source parametrisation, glottal waveform extraction techniques are reviewed. A performance study is carried out on three existing glottal inverse filtering approaches and results confirm that no single algorithm consistently outperforms others and provide a reliable and accurate estimate for different speech signals. The next step is modelling the extracted glottal flow. To more accurately estimate the glottal source parameters, a new time-domain LF-model fitting algorithm by extended Kalman filter is proposed. The algorithm is evaluated by comparing it with a standard time-domain method and a spectral approach. Results show the proposed fitting method is superior to existing fitting methods. To obtain accurate glottal source estimates for different speech signals, a multi-estimate (ME) fusion framework is proposed. In the framework different algorithms are applied in parallel to extract multiple sets of LF-model estimates which are then combined by quantitative data fusion. The ME fusion approach is implemented and tested in several ways. The novel fusion framework is shown to be able to give more reliable glottal LF-model estimates than any single algorithm

Finite sample properties of ARMA order selection

Applied Science