by Shiu-kwok Lau. / Bibliography: leaves 41-43 / Thesis (M.Ph.)--Chinese University of Hong Kong, 1985

Wong Kwan-to. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 50-52). / Abstracts in English and Chinese. / ACKNOWLEDGMENT --- p.iii / CHAPTER / Chapter ONE --- INTRODUCTION AND LITERATURE REVIEW --- p.1 / Chapter TWO --- THE MODEL --- p.3 / Chapter THREE --- ASYMPTOTIC BEHAVIOR OF THE LEAST SQUARES ESTIMATORS --- p.6 / Chapter FOUR --- EIGHT SPECIAL CASES --- p.12 / Chapter FIVE --- MONTE CARLO EXPERIMENTS --- p.36 / Chapter SIX --- CONCLUSION --- p.40 / APPENDIX --- p.41 / BIBLIOGRAPHY --- p.50

by Chun-Wai Sit. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 91-92). / Abstract also in Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Ridge Regression --- p.3 / Chapter 1.2 --- Generalized Ridge Regression and Present Work --- p.11 / Chapter Chapter 2 --- Shrinkage Estimation of Regression Coefficients --- p.14 / Chapter 2.1 --- Introduction --- p.15 / Chapter 2.2 --- Dominance over the Least Squares Estimator --- p.17 / Chapter 2.3 --- Dominance over the Ridge Estimator --- p.26 / Chapter 2.4 --- Bayesian Motivation --- p.31 / Chapter 2.5 --- Choosing the Shrinkage Factor --- p.33 / Chapter 2.5.1 --- Generalized Cross-Validation (GCV) --- p.34 / Chapter 2.5.2 --- RIDGM --- p.35 / Chapter 2.5.3 --- Iterative method for selecting the optimum parameter (IA) --- p.38 / Chapter 2.5.4 --- Empirical Bayes Approach --- p.45 / Chapter Chapter 3 --- Simulation Study --- p.47 / Chapter 3.1 --- Simulation Plan --- p.48 / Chapter 3.2 --- Simulation Result --- p.54 / Chapter 3.2.1 --- β = βL --- p.55 / Chapter 3.2.2 --- β = βs --- p.61 / Chapter 3.3 --- Average k and a --- p.67 / Chapter 3.3.1 --- Shrinkage Estimator --- p.67 / Chapter 3.3.2 --- Ridge Estimator --- p.78 / Chapter 3.4 --- Conclusion --- p.88 / References --- p.91

Digitized by Kansas Correctional Industries

A methodology for the construction of predictive empirical models of physiological characteristics from microarray data is presented. The method, applied here to the study of the development of diabetes and insulin resistance, can be further expanded to other cases and to also include a variety of other data, such as protein expression, or metabolic flux data. The importance of several of the genes identified by the modeling methodology can be verified by comparison with results from prior literature. This implies potentially significant roles in diabetes for several of the uncharacterized genes discovered during the modeling procedure. / Singapore-MIT Alliance (SMA)

In recent years, the main determinants of gold price have changed worldwide due to the increasing demand of gold. Additionally, TAIFEX provided investors with anothor trading instruments by launching US Dollar-denominated Gold Futures. Therefore, Taiwan gold market and stock market might interact more closely than before. The purpose of this study was to examine the endogenous relationship between gold price and stock price, and then analyze both markets with multi-equation simulation model and two-stage least squares method. The result shows that there is endogenous relationship between these two variables. Besides, depreciation of US dollar would lead to a rise in gold price which is denominated by US dollar. Exchange rate and stock price also moved in opposite directions. Finally, the launching of gold futures truly interested the investors and boosted the gold price.

Structural equation model (SEM) is a general approach to analyze multivariate data. It is a relatively comprehensive model and combines useful characteristics from many statistical approaches, thus enjoys a variety of advantages when dealing complex relationships. This report gives a brief introduction to SEM, focusing especially the comparison of SEM and OLS regression. A simple tutorial of how to apply SEM is also included with the introduction and comparison. SEM can be roughly seen as OLS regression added with features such as simultaneous estimation, latent factors and autocorrelation. Therefore, SEM enjoys a variety of advantages over OLS regression. However, it is not always the case that SEM will be the optimal choice. The biggest concern is the complexity of SEM, for simpler model will be preferable for researchers when the fitness is similar. Two simulation cases, one requires special features of SEM and one satisfies assumptions of OLS regression, are applied to illustrate the choice between SEM and OLS regression. A study using data from US life insurers in the year 1994 serves as a further illustration. The conclusion is when special features of SEM is required, SEM fits better and will be the better choice, while when OLS regression assumptions are satisfied, SEM and OLS regression will fit equally well, considering the complexity of SEM, OLS regression will be the better choice. / text

published_or_final_version / abstract / toc / Physics / Master / Master of Philosophy

Mud-pulse telemetry is a method used for measurement-while-drilling (MWD)in the oil industry. The telemetry signals are corrupted by spurious mud pump noise consisting of a large number of harmonically related sinusoids. In order to denoise the signal, the noise parameters have to be tracked accurately in real time. There are well established parametric estimation techniques for determining various parameters of independent sinusoids. The iterative methods based on the linear prediction properties of the sinusoids provide a computationally e±cient way of solving the non linear optimization problem presented by these methods. However, owing to the large number of these sinusoids, incorporating the harmonic relationship in the problem becomes important. This thesis is aimed at solving the problem of estimating parameters of harmonically related sinusoids. We examine the efficacy of IQML algorithm in estimating the parameters of the telemetry signal for varying SNRs and data lengths. The IQML algorithm proves quite robust and successfully tracks both stationary and slowly varying frequency signals. Later, we propose an algorithm for fundamental frequency estimation which relies on the initial harmonic frequency estimate. The results of tests performed on synthetic data that imitates real field data are presented. The analysis of the simulation results shows that the proposed method manages to remove noise causing sinusoids in the telemetry signal to a great extent. The low computational complexity of the algorithm also makes for an easy implementation on field where computational power is limited.

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