Semiparametric and nonparametric estimators are becoming indispensable tools in applied econometrics. Many of these estimators depend on the choice of smoothing bandwidth and kernel function. Optimality of such parameters is determined by unobservable smoothness of the model, that is, by differentiability of the distribution functions of random variables in the model. In this thesis we consider two estimators of this class: the smoothed maximum score estimator for binary choice models and the kernel density estimator. / We present theoretical results on the asymptotic distribution of the estimators under various smoothness assumptions and derive the limiting joint distributions for estimators with different combinations of bandwidths and kernel functions. Using these nontrivial joint distributions, we suggest a new way of improving accuracy and robustness of the estimators by considering a linear combination of estimators with different smoothing parameters. The weights in the combination minimize an estimate of the mean squared error. Monte Carlo simulations confirm suitability of this method for both smooth and non-smooth models. / For the original and smoothed maximum score estimators, a formal procedure is introduced to test for equivalence of the maximum likelihood estimators and these semiparametric estimators, which converge to the true value at slower rates. The test allows one to identify heteroskedastic misspecifications in the logit/probit models. The method has been applied to analyze the decision of married women to join the labour force.

Tübingen, Univ., Diss., 2008.

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

Dissertation (PhD)--University of Stellenbosch, 2008. / Bibliography. Also available via the Internet.

Thesis (Ph.D.)--City University of Hong Kong, 2009. / "Submitted to Department of Mathematics in partial fulfillment of the requirements for the degree of Doctor of Philosophy." Includes bibliographical references (leaves [73]-81)

Thesis (M.S.)--University of Florida, 2002. / Title from title page of source document. Document formatted into pages; contains x, 50 p.; also contains graphics. Includes vita. Includes bibliographical references.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2006. / Includes bibliographical references (leaves 42-46). Also available in electronic version.

The science of modelling the behaviour of natural phenomena and physical systems has grown significantly in importance in the last century. It helps us to understand and predict natural phenomena or improve and control all types of industrial processes. There are basically two approaches to system modelling: a model can be derived from physical knowledge of the system or by systematically testing it and estimating the model structure and parameters based on the test data. The method of system testing has gained importance due to the increasing complexity of modern industrial systems and processes. It also serves as a verification tool to the physical model. It has led to a rapid advance of a particular discipline within science generally referred to as system identification. This thesis deals with a frequency domain approach to identifying a particular class of nonlinear systems which can be modelled by the Volterra series. The methodology is based on the application of specially designed multisine test signals which allow second and third order terms of the Volterra series, so-called Volterra kernels, to be measured directly and the structure of the nonlinear system to be identified. In the first part of this thesis an introduction is given to system identification in the frequency domain and the analysis of a particular class of nonlinear systems using the Volterra series. Particular attention is given to the design of multisine signals and the development of a comprehensive software tool to aid with the identification task. The second part examines Volterra kernels and the application of block-oriented models to Volterra systems. A method is proposed for identifying the structure of such models based on Volterra kernels and in particular for the de-composition of a cascade structure into its linear dynamic components. The contributions made in this work include the development of a software tool for system identification, the measurement and representation of frequency domain Volterra kernels, as well as the classification and decomposition of block-oriented models by applying specially designed multisine signals.

In many instances of virtual machine deployments today, virtual machine instances are created to support a single application. Traditional operating systems provide an extensive framework for protecting one process from another. In such deployments, this protection layer becomes an additional source of overhead as isolation between services is provided at an operating system level and each instance of an operating system supports only one service. This makes the operating system the equivalent of a process from the traditional operating system perspective. Isolation between these operating systems and indirectly the services they support, is ensured by the virtual machine monitor in these deployments. In these scenarios the process protection provided by the operating system becomes redundant and a source of additional overhead. We propose a new model for these scenarios with operating systems that bypass this redundant protection offered by the traditional operating systems. We prototyped such an operating system by executing parts of the operating system in the same protection ring as user applications. This gives processes more power and access to kernel memory bypassing the need to copy data from user to kernel and vice versa as is required when the traditional ring protection layer is enforced. This allows us to save the system call trap overhead and allows application program mers to directly call kernel functions exposing the rich kernel library. This does not compromise security on the other virtual machines running on the same physical machine, as they are protected by the VMM. We illustrate the design and implementation of such a system with the Xen hypervisor and the XenoLinux kernel. / Science, Faculty of / Computer Science, Department of / Graduate

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