Thesis (Ph. D.)--Oregon State University, 1969. / Includes bibliographical references (p. 62-68). Also available on the World Wide Web.

[by] Arthur B. Baggeroer. / Bibliography: p. 187-191.

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

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M.Ing. (Electrical & Electronic Engineering) / Please refer to full text to view abstract

M.Ing. (Electrical And Electronic Engineering) / State estimation comprises the estimation of the position and velocity (state) of a target based on the processing of noise-corrupted measurements of its motion. This study views a class of measurement processes where the states are unobservable and cannot be estimated without placing additional constraints on the system. The bearings only target motion problem is taken as being representative of this type of problem. The results of this study indicate that practical state· estimation for systems with unobservable measurement processes is possible with the application of estimation theories and available estimation techniques. Due to the inherent nonlinear geometrical characteristics the problem is classified as a unobservable nonlinear estimation problem. A review of state estimation and estimation techniques is presented. The fundamental bearings only target motion concepts are discussed. A representative selection of bearings only estimators made from the published literature, is evaluated. The evaluation consists of a theoretical analysis and a Monte Carlo simulation of the estimators. Two realistic scenario's are considered. A classification framework is presented which may be useful to practical engineers in selecting suitable estimators. Batch estimators are shown to be more stable and likely to be used in bearings only applications than recursive estimators. The importance of isolating the unobservable states from the observable states by using a modified polar co-ordinate system, is stressed. It is also shown that effective data processing can be achieved by using all available measurements and a maximum likelihood estimator.

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Previous research implementing stratification on the propensity score has generally relied on using five strata, based on prior theoretical groundwork and minimal empirical evidence as to the suitability of quintiles to adequately reduce bias in all cases and across all sample sizes. This study investigates bias reduction across varying number of strata and sample sizes via a large-scale simulation to determine the adequacy of quintiles for bias reduction under all conditions. Sample sizes ranged from 100 to 50,000 and strata from 3 to 20. Both the percentage of bias reduction and the standardized selection bias were examined. The results show that while the particular covariates in the simulation met certain criteria with five strata that greater bias reduction could be achieved by increasing the number of strata, especially with larger sample sizes. Simulation code written in R is included.

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