<p>The validation of software quality metrics lacks statistical</p><p>significance. One reason for this is that the data collection</p><p>requires quite some effort. To help solve this problem,</p><p>we develop tools for metrics analysis of a large number of</p><p>software projects (146 projects with ca. 70.000 classes and</p><p>interfaces and over 11 million lines of code). Moreover, validation</p><p>of software quality metrics should focus on relevant</p><p>metrics, i.e., correlated metrics need not to be validated independently.</p><p>Based on our statistical basis, we identify correlation</p><p>between several metrics from well-known objectoriented</p><p>metrics suites. Besides, we present early results of</p><p>typical metrics values and possible thresholds.</p>

We use inequalities to design short universal algorithms that can be used to generate random variates from large classes of univariate continuous or discrete distributions (including all log-concave distributions). The expected time is uniformly bounded over all these distributions for a particular generator. The algorithms can be implemented in a few lines of high level language code. (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

In the overlay Cognitive Radio (CR) networks, the low priority Secondary Users (SUs) must constantly monitor the occupied spectrum to detect the possible appearances of the high priority Primary Users (PUs) within the same spectrum portion. On detection, the SUs must vacate the occupied spectrum portion without interfering with the PUs beyond a certain threshold duration and must opportunistically access another idle spectrum portion to guarantee their seamless communication. This mechanism is known as the spectrum handoff process. In this thesis, we first introduce a novel approach to model the CR channel which is capable of capturing a more realistic behavior of the spectrum occupancy by both user types and that is more suitable for modeling the spectrum handoff process as opposed to the existing approaches. Then using that as a base we focus on building analytical models to capture the various aspects of the spectrum handoff process in a realistic manner.

In the overlay Cognitive Radio (CR) networks, the low priority Secondary Users (SUs) must constantly monitor the occupied spectrum to detect the possible appearances of the high priority Primary Users (PUs) within the same spectrum portion. On detection, the SUs must vacate the occupied spectrum portion without interfering with the PUs beyond a certain threshold duration and must opportunistically access another idle spectrum portion to guarantee their seamless communication. This mechanism is known as the spectrum handoff process. In this thesis, we first introduce a novel approach to model the CR channel which is capable of capturing a more realistic behavior of the spectrum occupancy by both user types and that is more suitable for modeling the spectrum handoff process as opposed to the existing approaches. Then using that as a base we focus on building analytical models to capture the various aspects of the spectrum handoff process in a realistic manner.

We investigate various aspects of the proton structure in this thesis. The first addresses the distribution of the proton spin among its constituents, quarks and gluons. We derive the framework of distribution functions for these constituents and study the properties of the polarized distributions which describe the spin structure of the proton. A determination of the polarized distributions on the basis of present experimental data is presented and options for future measurements are critically evaluated. A second aspect under consideration is the phenomenology of hard diffractive electron-proton scattering. We show how diffractive interaction and hard scattering can be disentangled and suggest experimental tests for this interpretation. Finally, we illustrate how the knowledge on the proton structure can be used for the computation of observables in proton-antiproton collisions, confirming or extending our knowledge of the physics of elementary particles.

[Abstract]: This thesis investigates the prediction distributions of future response(s), conditional on a set of realized responses for some linear models havingstudent-t error distributions by the Bayesian approach under the uniform priors. The models considered in the thesis are the multiple regression modelwith multivariate-t errors and the multivariate simple as well as multiple re-gression models with matrix-T errors. For the multiple regression model, results reveal that the prediction distribution of a single future response anda set of future responses are a univariate and multivariate Student-t distributions respectively with appropriate location, scale and shape parameters.The shape parameter of these prediction distributions depend on the size of the realized responses vector and the dimension of the regression parameters' vector, but do not depend on the degrees of freedom of the error distribu-tion. In the multivariate case, the distribution of a future responses matrix from the future model, conditional on observed responses matrix from the realized model for both the multivariate simple and multiple regression mod-els is matrix-T distribution with appropriate location matrix, scale factors and shape parameter. The results for both of these models indicate that prediction distributions depend on the realized responses only through the sample regression matrix and the sample residual sum of squares and products matrix. The prediction distribution also depends on the design matricesof the realized as well as future models. The shape parameter of the prediction distribution of the future responses matrix depends on size of the realized sample and the number of regression parameters of the multivariatemodel. Furthermore, the prediction distributions are derived by the Bayesian method as multivariate-t and matrix-T are identical to those obtained under normal errors' distribution by the di®erent statistical methods such as the classical, structural distribution and structural relations of the model approaches. This indicates not only the inference robustness with respect todepartures from normal error to Student-t error distributions, but also indicates that the Bayesian approach with a uniform prior is competitive withother statistical methods in the derivation of prediction distribution.

Thesis (M.S.)--Virginia Polytechnic Institute, 1966. / Also available via the Internet.

Thesis (Ph.D.) - University of St Andrews, January 2007.

Thesis (Ph. D.)--University of Washington, 1998. / Vita. Includes bibliographical references (p. [77]-80).

Thesis (Ph.D.)--University of Delaware, 2007. / Principal faculty advisor: Henry R. Glyde, Dept. Physics & Astronomy. Includes bibliographical references.