Li Johnson Ching Hong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 40-42). / Abstracts in English and Chinese. / ACKNOWLEDGEMENT --- p.2 / ABSTRACT --- p.3 / CHINESE ABSTRACT --- p.4 / TABLE OF CONTENTS --- p.5 / Chapter CHAPTER 1 --- INTRODUCTION --- p.7 / Thorndike's Three Formulae to Correct Correlation for Range Restriction --- p.8 / Significance of Case 3 --- p.9 / Importance of Standard Error and Confidence Intervals --- p.10 / Research Gap in the Estimation of Standard Error of rc and the Construction of the Confidence Intervals for pxy --- p.10 / Objectives of the Present Study --- p.12 / Chapter CHAPTER 2 --- BOOTSTRAP METHOD --- p.13 / Different Confidence Intervals Constructed for the Present Study --- p.14 / Chapter CHAPTER 3 --- A PROPOSED PROCEDURE FOR THE ESTIMATION OF STANDAR ERROR OF rc AND THE CONSTRUCTION OF CONFIDENCE INTERVALS --- p.16 / Chapter CHAPTER 4 --- METHODS --- p.20 / Model Specifications --- p.20 / Procedure --- p.21 / Chapter CHAPTER 5 --- ASSESSMENT --- p.23 / Chapter CHAPTER 6 --- RESULTS --- p.25 / Accuracy of Average Correlation Corrected for IRR ( rc ) --- p.25 / Empirical Standard Deviation (SDE) of rc --- p.29 / Accuracy of Standard Error Estimate --- p.29 / Accuracy of Confidence Intervals --- p.33 / Chapter CHAPTER 7 --- DISCUSSION AND CONCLUSION --- p.36 / Chapter CHAPTER 8 --- LIMITATIONS AND FURTHER DIRECTIONS --- p.38 / REFERENCES --- p.40 / APPENDIX A --- p.43 / FIGURE CAPTION --- p.53 / LIST OF TABLES --- p.55

Wong Chun Kit. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves [132-143]). / Abstracts in English and Chinese. / List of Tables --- p.i / List of Figures --- p.iv / List of Symbols --- p.v / Chapter CHAPTER ONE: --- INTRODUCTION --- p.1 / Chapter 1.1 --- Objectives and Significance of the Study --- p.4 / Chapter 1.2 --- Physical Setting of Hong Kong --- p.5 / Chapter 1.3 --- Climate of Hong Kong --- p.9 / Chapter 1.4 --- Data Acquisition --- p.11 / Chapter 1.4.1 --- Raingauges in Hong Kong --- p.11 / Chapter 1.4.2. --- Database for the Spatial Variation Analyses --- p.14 / Chapter 1.4.2.1. --- Data Selection for the Analyses for Factors Affecting Rainfall ´ؤ Elevation and Aspect --- p.15 / Chapter 1.4.2.2. --- Data Selection for the Classification of Stations and Inter-station Correlation Analysis --- p.17 / Chapter 1.4.3 --- Database for the Temporal Variation Analyses --- p.20 / Chapter CHAPTER TWO : --- LITERATURE REVIEW --- p.22 / Chapter 2.1 --- Spatial Variation of Rainfall --- p.22 / Chapter 2.2 --- Detection of Temporal Changes in Rainfall --- p.28 / Chapter 2.3 --- Urban Influence on Rainfall --- p.29 / Chapter 2.4 --- Studies in Hong Kong --- p.33 / Chapter CHAPTER THREE : --- METHODOLOGY --- p.33 / Chapter 3.1 --- Preliminary Processing of the Data --- p.38 / Chapter 3.2 --- Data Analysis --- p.40 / Chapter 3.2.1 --- General Pattern of Rainfall Distribution --- p.40 / Chapter 3.2.2 --- Data Analyses of Spatial Variation --- p.41 / Chapter 3.2.2.1 --- Correlation between Rainfall and Elevation --- p.41 / Chapter 3.2.2.2 --- Correlation between Rainfall and Aspect --- p.42 / Chapter 3.2.2.3 --- Classification of Stations --- p.43 / Chapter 3.2.2.4 --- Inter-Station Correlation Analysis --- p.46 / Chapter 3.2.3 --- Data Analysis of Temporal Variation --- p.46 / Chapter 3.2.3.1 --- The Running Mean Method --- p.47 / Chapter 3.2.3.2 --- The 'Standard Error of the Difference' Test --- p.49 / Chapter CHAPTER FOUR: --- RESULTS AND DISCUSSION --- p.50 / Chapter 4.1 --- Graphical Representation of Spatial Rainfall Pattern --- p.50 / Chapter 4.1.1 --- Annual Rainfall Pattern --- p.50 / Chapter 4.1.2 --- Monthly Rainfall Pattern --- p.56 / Chapter 4.1.3 --- Frequency Distribution of Raindays --- p.59 / Chapter 4.1.4 --- Pentade Rainfall Pattern --- p.64 / Chapter 4.1.5 --- Diurnal Rainfall Pattern --- p.67 / Chapter 4.1.6 --- Implications of the Spatial Rainfall Pattern --- p.70 / Chapter 4.2 --- Analyses of Spatial Variation in Rainfall --- p.78 / Chapter 4.2.1 --- Relationship between Rainfall and Elevation --- p.78 / Chapter 4.2.2 --- Relationship between Rainfall and Aspect --- p.82 / Chapter 4.2.3 --- Classification of Stations --- p.85 / Chapter 4.2.3.1 --- Principal Components Interpretation --- p.87 / Chapter 4.2.3.2 --- Result of Classification --- p.90 / Chapter 4.2.4 --- Inter-Station Correlation Analysis --- p.98 / Chapter 4.2.5 --- Discussion of the Rainfall Spatial Variation --- p.103 / Chapter 4.3 --- Analyses of Temporal Variation in Rainfall --- p.107 / Chapter 4.3.1 --- Annual Rainfall --- p.107 / Chapter 4.3.2 --- Monthly Rainfall --- p.110 / Chapter 4.3.3 --- Pentade Rainfall --- p.112 / Chapter 4.3.4 --- Diurnal Rainfall --- p.117 / Chapter 4.3.5 --- Discussion of the Rainfall Temporal Variation --- p.118 / Chapter CHAPTER FIVE: --- CONCLUSIONS AND RECOMMENDATIONS --- p.126 / Chapter 5.1 --- Summary of Findings --- p.126 / Chapter 5.2 --- Limitation of this Research --- p.129 / Chapter 5.3 --- Prospects of this Research --- p.130 / Bibliography

by Au Wai Shan, Christine, Choi Wing Kam. / Thesis (M.B.A.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 86-90). / ABSTRACT --- p.ii / ACKNOWLEDGEMENT --- p.iv / TABLE OF CONTENTS --- p.v / LIST OF TABLES --- p.vii / CHAPTER / Chapter I. --- INTRODUCTION --- p.1 / Chapter II. --- LITERATURE REVIEW --- p.3 / Chapter III. --- METHODOLOGY --- p.9 / Chapter 1 --- Source of Data and Company Information --- p.9 / Chapter 1.1 --- Data on Security Prices and Macroeconomic Variables --- p.9 / Chapter 1.2 --- Company Annual Reports --- p.9 / Chapter 1.3 --- "Journals, Newspapers and Related Magazines" --- p.10 / Chapter 2. --- Selection of Company --- p.10 / Chapter 3. --- "Whatts PanEL,Data?" --- p.10 / Chapter 3.1 --- Benefits of using Panel Data --- p.11 / Chapter 3.2 --- Limitations of using Panel Data --- p.12 / Chapter 4. --- Multiple regression analysts --- p.13 / Chapter 4.1 --- What is multiple regression model? --- p.13 / Chapter 4.2 --- Assumptions of multiple regression --- p.15 / Chapter 5. --- FtnanctaL RatIo Analysts --- p.16 / Chapter 6. --- Economic Factor Analysis --- p.19 / Chapter IV. --- FINDINGS --- p.20 / Chapter 1. --- ResuLts of MULttpte Regression (By Individual Company) of the Stock Price and MacRoeconomtc factors --- p.20 / Chapter 1.1 --- "R2, Coefficients of variables and F-statistic" --- p.20 / Chapter 1.2 --- Correlation Among the Macroeconomic Factors --- p.23 / Chapter 2. --- Results of MULTIpLe REgREssIons (By Sectors) of thE Stock Prtce and Financial Statement RatIos --- p.24 / Chapter 2.1 --- "R2, Coefficients of variables and F-statistic" --- p.24 / Chapter 2.2 --- Correlation among the Micro-economic Factors --- p.25 / Chapter V. --- DISCUSSIONS --- p.27 / Chapter 1. --- Summary of findings --- p.27 / Chapter 2. --- Discusston of the impact of economic factors on the stock price --- p.28 / Chapter 3. --- "Dtscusston the impacts of ftnancial, statement ratios on the stock price" --- p.29 / Chapter 4. --- LImItattons on our model --- p.31 / Chapter 4.1 --- Outlier Problems --- p.31 / Chapter 4.2 --- Average stock price in the month of announcing annual reports --- p.31 / Chapter 4.3 --- Using of annual data --- p.32 / Chapter VI. --- FURTHER DISCUSSION ON NOWADAYS PHENOMENA --- p.33 / Chapter 1. --- Greenspan's Theory --- p.33 / Chapter 2. --- ThE FEvER of Internet/ TEchnoLOgy/ConcEPt Stock --- p.34 / Chapter VII. --- RECOMMENDATIONS --- p.35 / Chapter 1. --- Other mEthodoLogIEs --- p.35 / Chapter 2. --- Other Ratios with same or similar meanings --- p.36 / Chapter 3. --- Other indices --- p.37 / Chapter 4. --- A new standard： sustatnaBILIty --- p.37 / Chapter VIII. --- CONCLUSION --- p.39 / APPENDIX --- p.40 / BIBLIOGRAPHY --- p.86

by Kwok-Fai Mo. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 74-79). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Statistical Modeling --- p.5 / Chapter 2.1 --- Poisson Regression --- p.5 / Chapter 2.2 --- Overdispersion and Autorrelation --- p.7 / Chapter 2.3 --- Generalized Estimating Equation --- p.9 / Chapter 2.4 --- Zeger's Mehthod --- p.12 / Chapter 2.5 --- Multicollinearity --- p.18 / Chapter 2.5.1 --- The Modified Generalized Estimating Equation --- p.18 / Chapter 2.6 --- Bootstrapping Method --- p.21 / Chapter 2.7 --- The Bootstrap Choice of Ridge Parameter --- p.23 / Chapter 3 --- The Robustness of Zeger's Approach to the Specification of ηt - Simulation Study --- p.26 / Chapter 3.1 --- Introduction --- p.26 / Chapter 3.2 --- Zeger's Algorithm with Varoious Time Series Data --- p.27 / Chapter 3.2.1 --- Data without Multicollinearity --- p.27 / Chapter 3.2.1 --- Data with Multicollinearity --- p.34 / Chapter 3.3 --- Modified Generalized Estimating Equation Approach --- p.40 / Chapter 3.3 --- The Choice of Ridge Paramter in Bootstrap --- p.42 / Chapter 4 --- Real Example --- p.46 / Chapter 4.1 --- Data Structure --- p.46 / Chapter 4.2 --- Model Building --- p.49 / Chapter 4.3 --- Single Pollutant Model --- p.57 / Chapter 4.4 --- Multiple Pollutant Model --- p.62 / Chapter 5 --- Conclusion and Discussion --- p.64 / Appendix --- p.69 / References --- p.74

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by Sun Tong Lau. / M.S.

Thesis (Nav. Arch.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering; and, (M.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1975. / Bibliography: leaves 248-249. / M.S. / Nav. Arch.

Latent variable models (LVMS) are widely appreciated multivariate methods to explore variables that are related to the observed variables, and assessing the relationships among them. One of most widely used latent variable models is structural equation model (SEM). Based on more than a dozen standard packages for fitting SEMs, such as LISREL VIII (Jorskog and Sorbom, 1996), and EQS (Bentler, 2004), these models have been widely appreciated in behavioral, educational, medical, social, and psychological research. The statistical theories and methods in these packages are based on the normal distribution; hence, they are vulnerable to outliers and the non-normal assumption. As outliers and non-normal data set are commonly encountered in substantive research, this fundamental problem has received much attention in the field. However, almost all existing methods are developed via the covariance structure analysis approach that heavily depends on the asymptotical properties of the sample covariance matrices S. Hence, this approach cannot be applied to the more complex SEMs and/or SEMs with more complex data structure such as missing data, because under these more complicated situations S is complicated, and its asymptotical properties are not well known. The objectives of this thesis are to develop novel robust methods for analyzing complex SEMs and/or more data structures, including but not limited to nonlinear SEMs with missing data. Both maximum likelihood (ML) and Bayesian approaches for estimation, hypothesis testing and model comparison will be investigated. Efficient algorithm for computing the results for statistical inference will be developed through unitization and modification of the advanced tools in statistical computing, for example the Monte Carlo Expectation-Maximization algorithm, and the Markov Chains Monte Carlo methods. Asymptotical properties of some statistics are derived. Simulation studies and real examples are conducted to reveal the empirical performance of the Bayesian and ML approaches. The newly developed methodologies will be very useful for analyzing complex data in the substantive research. / Xia Yemao. / "October 2005." / Adviser: S. Y. Lee. / Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 3883. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (p. 105-114). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract in English and Chinese. / School code: 1307.

No description available.

Wilbur B. Davenport, Jr. / "March 8, 1951." / Bibliography: p. 16. / Army Signal Corps Contract No. W36-039-sc-32037 Project No. 102B. Dept. of the Army Project No. 3-99-10-022.

When there are a large number of predictors and few observations, building a regression model to explain the behavior of a response variable such as a patient's medical condition is very challenging. This is a "p ≫n " variable selection problem encountered often in modern applied statistics and data mining. Chapter one of this thesis proposes a rigorous procedure which groups predictors into clusters of "highly-correlated" variables, selects a representative from each cluster, and uses a subset of the representatives for regression modeling. The proposed Penalized method based on Representatives (PR) extends the Lasso for the p ≫ n data and highly correlated variables, to build a sparse model practically interpretable and maintain prediction quality. Moreover, we provide the PR-Sequential Grouped Regression (PR-SGR) to make computation of the PR procedure efficient. Simulation studies show the proposed method outperforms existing methods such as the Lasso/Lars. A real-life example from a mental health diagnosis illustrates the applicability of the PR-SGR. In the second part of the thesis, we study the analysis of time-to-event data called a gap data when missing time intervals (gaps) possibly happen prior to the first observed event time. If a gap occurs prior to the first observed event, then the first observed event may or may not be the first true event. This incomplete knowledge makes the gap data different from the well-studied regular interval censored data. We propose a Non-Parametric Estimate for the Gap data (NPEG) to estimate the survival function for the first true event time, derive its analytic properties and demonstrate its performance in simulations. We also extend the Imputed Empirical Estimating method (IEE), which is an existing nonparametric method for the gap data up to one gap, to handle the gap data with multiple gaps.