Thesis (Ph. D.)--Michigan State University. Dept. of Counseling, Educational Psychology and Special Education, 1998. / Title from PDF t.p. (viewed on July 2, 2009) Includes bibliographical references (p. 148-149). Also issued in print.

published_or_final_version / abstract / Statistics and Actuarial Science / Master / Master of Philosophy

by May So-Ching Lam. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 87-88). / Abstracts in English and Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The importance of Vector Time Series Analysis --- p.1 / Chapter 1.2 --- Objective --- p.3 / Chapter Chapter 2 --- Vector Autoregressive Models --- p.5 / Chapter 2.1 --- The VAR(p) models --- p.5 / Chapter 2.2 --- Least square estimation method --- p.7 / Chapter 2.3 --- VAR forecast --- p.9 / Chapter Chapter 3 --- Model Selection Criteria --- p.12 / Chapter 3.1 --- VAR order selection methods --- p.12 / Chapter 3.2 --- Hsiao's sequential method --- p.17 / Chapter 3.2.1 --- Two variables case --- p.19 / Chapter 3.2.2 --- Three variables case --- p.24 / Chapter Chapter 4 --- Illustrative Examples --- p.32 / Chapter Chapter 5 --- A Simulation Study --- p.37 / Chapter 5.1 --- Designs of experiments --- p.37 / Chapter 5.2 --- Simulation results --- p.47 / Chapter Chapter 6 --- Summary --- p.53 / Tables --- p.55 / References --- p.87

Thesis (Ph. D.)--University of California, San Diego, 2001. / Vita. Includes bibliographical references (leaves 116-120).

Thesis (M. Phil.)--University of Hong Kong, 2007. / Also available in print.

Thesis (M.S.) -- Worcester Polytechnic Institute. / Keywords: robust; CUSUM test; SETAR; nonlinearity. Includes bibliographical references (p. 79-82 ).

Thesis (Ph. D.)--University of Chicago, Graduate School of Business, March 2001. / Includes bibliographical references. Also available on the Internet.

The goal of this thesis work was to study the characteristics of the EEG signal and then, based on the insights gained from these studies, pursue an initial investigation into a processing method that would extract useful event related information from single trial EEG. The fundamental tool used to study the EEG signal characteristics was autoregressive modeling. Early investigations pointed to the need to employ robust techniques in both model parameter estimation and signal estimation applications. Pursuing robust techniques ultimately led to the development of a single trial processing method which was based on a simple neurological model that assumed an additive outlier nature of event related potentials to the ongoing EEG process. When event related potentials, such as motor related potentials, are generated by a unique additional process they are "added" into the ongoing process and hence, will appear as additive outlier content when considered from the point of view of the ongoing process. By modeling the EEG with AR models with robustly estimated (GM-estimates) parameters and by using those models in a robust signal estimator, a "cleaned" EEG signal is obtained. The outlier content, data that is extracted from the EEG during cleaning, is then processed to yield event related information. The EEG from four subjects formed the basis of the initial investigation into the viability of this single trial processing scheme. The EEG was collected under two conditions: an active task in which subjects performed a skilled thumb movement and an idle task in which subjects remained alert but did not carry out any motor activity. The outlier content was processed which provided single trial outlier waveforms. In the active case these waveforms possessed consistent features which were found to be related to events in the individual thumb movements. In the idle case the waveforms did not contain consistent features. Bayesian classification of active trials versus idle trials was carried out using a cost statistic resulting from the application of dynamic time warping to the outlier waveforms. Across the four subjects, when the decision boundary was set with the cost of misclassification equal, 93% of the active trials were classified correctly and 18% of the idle trials were incorrectly classified as active. When the cost of misclassifying an idle trial was set to be five times greater, 80% of the active trials were classified correctly and only 1.7% of the idle trials were incorrectly classified as active. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

Ip Ching-Tak. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 68-70). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The Importance of Time Series Analysis with Outliers --- p.1 / Chapter 2 --- Outlier Analysis in Time Series --- p.4 / Chapter 2.1 --- Basic Idea --- p.4 / Chapter 2.2 --- Outliers in Time Series --- p.6 / Chapter 2.2.1 --- One Outlier Case --- p.6 / Chapter 2.2.2 --- Multiple Outliers Case --- p.8 / Chapter 2.3 --- Outlier Identification --- p.9 / Chapter 2.3.1 --- Outlier Detection of One Outlier Case --- p.9 / Chapter 2.3.2 --- Case of Unknown Model Parameters --- p.10 / Chapter 2.3.3 --- Iterative Identification Procedure --- p.10 / Chapter 3 --- ARMA Model Forecasting --- p.13 / Chapter 3.1 --- Unknown Model Problem --- p.13 / Chapter 3.1.1 --- AR Approximation --- p.14 / Chapter 3.1.2 --- ARMA Approximation --- p.15 / Chapter 3.1.3 --- "Comparison of AIC, AICC and BIC" --- p.16 / Chapter 3.2 --- A Simulation Study --- p.19 / Chapter 3.2.1 --- Results for One-Step-Ahead Forecast --- p.20 / Chapter 3.2.2 --- Results for the Mean of Multiple Forecasts --- p.22 / Chapter 4 --- ARIMA Model Forecasting --- p.24 / Chapter 4.1 --- Effect of Differencing on Time Series --- p.24 / Chapter 4.1.1 --- Outlier Free Model --- p.24 / Chapter 4.1.2 --- Outlier Model --- p.25 / Chapter 4.2 --- Unknown Model Problem --- p.28 / Chapter 4.2.1 --- AR Approximation --- p.28 / Chapter 4.2.2 --- ARMA Approximation --- p.28 / Chapter 4.3 --- Unknown Differencing Case --- p.29 / Chapter 4.4 --- A Simulation Study --- p.29 / Chapter 4.4.1 --- Results for One-Step-Ahead Forecast --- p.30 / Chapter 4.4.2 --- Results for the Mean of Multiple Forecasts --- p.32 / Chapter 5 --- Illustrative Examples --- p.34 / Chapter 5.1 --- Examples of Stationary Time Series --- p.34 / Chapter 5.1.1 --- Example 1 --- p.34 / Chapter 5.1.2 --- Example 2 --- p.36 / Chapter 5.2 --- Examples of Nonstationary Time Series --- p.37 / Chapter 5.2.1 --- Example 3 --- p.37 / Chapter 5.2.2 --- Example 4 --- p.38 / Chapter 6 --- Conclusion --- p.40 / Chapter A --- "Comparison of AIC, AICC and BIC" --- p.42 / Chapter A.1 --- AR Approximation Results --- p.42 / Chapter A.2 --- ARMA Approximation Results --- p.45 / Chapter B --- Simulation Results for ARMA Models --- p.47 / Chapter C --- Simulation Results for ARIMA Models --- p.56 / Chapter D --- SACF and SPACF of Examples --- p.65 / Bibliography --- p.68

Includes bibliographical references (p. 29-30).