Thesis (M. Sc.)--Carleton University, 2000. / Includes bibliographical references. Also available in electronic format on the Internet.

Thesis (Ph. D.)--University of Chicago, 1931. / Lithographed. "Private edition, distributed by the University of Chicago libraries." Bibliography: p. 67-69.

Project (M.S.)--Brigham Young University. Dept. of Statistics, 2006. / Includes bibliographical references (p. 45-46).

Thesis (M.S.)--Oregon State University, 2006. / Printout. Includes bibliographical references (leaves 72-77). Also available on the World Wide Web.

Thesis (Ph. D.)--Illinois State University, 1997. / Title from title page screen, viewed June 8, 2006. Dissertation Committee: Graham A. Jones, John A. Dossey (co-chairs), Robert L. Fisher, Cynthia W. Langrall, Jane O. Swafford. Includes bibliographical references (leaves 251-257) and abstract. Also available in print.

Thesis (Ph.D.)--McMaster University, 2003. / Advisor: Zhi-Quan (Tom) Luo. Includes bibliographical references (leaves 214-217). Also available via World Wide Web.

Thesis (M.A.)--Bowling Green State University, 2007. / Document formatted into pages; contains ix, 44 p. Includes bibliographical references.

The severity of the outbreak of an infectious disease is highly dependent upon the structure of the population through which it spreads. This thesis considers the stochastic SIR (susceptible → infective → removed) household epidemic model, in which individuals mix with other individuals in their household at a far higher rate than with any other member of the population. This model gives a more realistic view of dynamics for the transmission of many diseases than the traditional model, in which all individuals in a population mix homogeneously, but retains mathematical tractability, allowing us to draw inferences from disease data. This thesis considers inference from epidemics using data which has been acquired after an outbreak has finished and whilst it is still in its early, `emerging' phase. An asymptotically unbiased method for estimating within household infectious contact rate(s) from emerging epidemic data is developed as well as hypothesis testing based on final size epidemic data. Finally, we investigate the use of both emerging and final size epidemic data to estimate the vaccination coverage required to prevent a large scale epidemic from occurring. Throughout the thesis we also consider the exact form of the households epidemic model which should be used. Specifically, we consider models in which the level of infectious contact between two individuals in the same household varies according to the size of their household.

Nessa dissertação estudamos Sistemas Dinâmicos do ponto de vista da Otimização Ergódica. Analizamos o problema da maximização da integral de potenciais com respeito a probabilidades invariantes pela dinâmica. Mostramos que toda medida ergódica e unicamente maximizante para algum potencial. Verificamos que o conjunto de potenciais com exatamente uma medida maximizadora e residual. Esses resultados são obtidos atrav es de técnicas da Teoria Ergódica e Análise Convexa. / In this thesis we study dynamical systems trough the viewpoint of ergodic optimization. We analyze the problem of maximizing integrals of potentials with respect to invariant probabilities. We show that every ergodic measure is uniquely maximizing for some potential. We also verify that the set of potentials with exactly one maximizing measure is residual. This results are obtained through techniques of ergodic theory and convex analysis.

Liu, Li. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 41-44). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Empirical Likelihood --- p.1 / Chapter 1.2 --- Empirical Likelihood for Dependent Data --- p.4 / Chapter 1.2.1 --- Spectral Method --- p.5 / Chapter 1.2.2 --- Blockwise Method --- p.6 / Chapter 1.3 --- Edgeworth Expansions and Bartlett Correction --- p.9 / Chapter 1.3.1 --- Coverage Errors --- p.10 / Chapter 1.3.2 --- Edgeworth Expansions --- p.11 / Chapter 1.3.3 --- Bartlett Correction --- p.13 / Chapter 2 --- Bartlett Correction for EL --- p.16 / Chapter 2.1 --- Empirical Likelihood in Time Series --- p.16 / Chapter 2.2 --- Stochastic Expansions of EL in Time Series --- p.19 / Chapter 2.3 --- Edgeworth Expansions of EL in Time Series --- p.22 / Chapter 2.3.1 --- Validity of the Formal Edgeworth Expansions --- p.22 / Chapter 2.3.2 --- Cumulant Calculations --- p.24 / Chapter 2.4 --- Main Results --- p.30 / Chapter 3 --- Simulations --- p.32 / Chapter 3.1 --- Confidence Region --- p.33 / Chapter 3.2 --- Coverage Error of Confidence Regions --- p.35 / Chapter 4 --- Conclusion and Future Work --- p.38 / Bibliography --- p.41