In microarray analysis, people are interested in those features that have different characters in diseased samples compared to normal samples. The usual p-value method of selecting significant genes either gives too many false positives or cannot detect all the significant features. The False Discovery Rate (FDR) method controls false positives and at the same time selects significant features. We introduced Benjamini's method and Storey's method to control FDR, applied the two methods to human Meningioma data. We found that Benjamini's method is more conservative and that, after the number of the tests exceeds a threshold, increase in number of tests will lead to decrease in number of significant genes. In the second chapter, we investigate ways to search interesting gene expressions that cannot be detected by linear models as t-test or ANOVA. We propose a novel approach to use quadratic logistic regression to detect genes in Meningioma data that have non-linear relationship within phenotypes. By using quadratic logistic regression, we can find genes whose expression correlates to their phenotypes both linearly and quadratically. Whether these genes have clinical significant is a very interesting question, since these genes most likely be neglected by traditional linear approach.

Yiu Tat-choi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 68-69). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Student Ratings of Instructors --- p.2 / Chapter 1.2 --- Research Plan and Difficulties Encountered in the Study --- p.4 / Chapter 2 --- Data and An Overall Picture of Study --- p.7 / Chapter 2.1 --- The Questionnaire and Data Collection Method --- p.7 / Chapter 2.2 --- Pilot Study --- p.8 / Chapter 2.3 --- Data Editing --- p.12 / Chapter 2.3.1 --- Clerical Error --- p.12 / Chapter 2.3.2 --- Strange Patterns --- p.13 / Chapter 2.4 --- Missing Items ´ؤ Item Nonresponse --- p.14 / Chapter 2.5 --- Missing Items - Unit Nonresponse --- p.16 / Chapter 2.6 --- Effective Class Size --- p.21 / Chapter 2.7 --- Imputation of Item Nonresponse Data --- p.23 / Chapter 2.8 --- Overall Picture of Study --- p.25 / Chapter 3 --- Data Analysis I: Logistic Regression --- p.28 / Chapter 3.1 --- Conditional Independence --- p.29 / Chapter 3.2 --- Partial Correlation --- p.30 / Chapter 3.3 --- Simultaneous p-value --- p.31 / Chapter 3.4 --- Logit Model --- p.32 / Chapter 3.5 --- Logit Model for Ordinal Variables --- p.35 / Chapter 3.6 --- Iteratively Reweighted Least Squares (IRLS) Algorithm --- p.36 / Chapter 3.7 --- Criteria for Assessing Model Fit --- p.38 / Chapter 3.7.1 --- Assessing the Fit of the Model --- p.39 / Chapter 3.7.2 --- Pearson Chi-Square and Deviance --- p.40 / Chapter 3.8 --- Interpretation of the Coefficients of The Weighted Logistic Re- gression Model --- p.42 / Chapter 3.8.1 --- Nominal Independent Variable --- p.42 / Chapter 3.8.2 --- Continuous Independent Variable --- p.45 / Chapter 4 --- Data Analysis II: Adjusted Instructor Score --- p.49 / Chapter 4.1 --- Removing Effects of Class Characteristics Factor and Adjust- ing the Score --- p.50 / Chapter 4.2 --- Adjusted Instructor Score (AIS) --- p.54 / Chapter 4.3 --- Estimate Standard Error of AIS by Bootstrap Method --- p.55 / Chapter 5 --- Conclusion --- p.58 / Chapter 5.1 --- Comparison Between the AIS and Average Score --- p.58 / Chapter 5.2 --- Discussion --- p.60 / Appendix A1: Course Evaluation Survey Form --- p.63 / Appendix A2: Course Evaluation Supplementary Form . --- p.64 / Appendix B: Descriptive Statistics for Response Rate --- p.65 / Appendix C: The Descriptions of Class Characteristics Dummy Variables --- p.67 / Bibliography --- p.68

A central topic in empirical asset pricing is how to explain anomalies in various trading horizons. This dissertation contains two essays that study several anomalies in medium-term/long-term investment in the equity market and in high-frequency trading in the foreign exchange market. In the first essay, I propose an investor underreaction model with heterogeneous truncations across time and stocks. In this setting, investors are more attracted to dramatic changes in stock prices than to gradual changes. Continuous information causes signals to be truncated which delays their incorporation into stock prices thus generating momentum. Under the assumption that investors are more attracted to winner stocks and ignore more information in loser stocks, I show that a loser portfolio exhibits stronger momentum and higher profitability than a winner portfolio with the same discreteness level. A trading strategy based on this model yields high alphas and Sharpe ratios. Evidence from social media trends aligns well with this model. In the second essay, I develop multivariate logistic models to explain the short-term offer price movement of the currency pair EUR/USD from the EBS limit order book. Using logistic regression based methods, I study the impact of various market microstructure factors on offer price changes in the next second. The empirical results show explanatory power for the testing sample up to 45% and a true positive rate of the prediction up to 87%. The model reveals interesting mechanisms for the underlying driving forces of the tick-by-tick currency price movement.

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The research of corporate financial distress prediction model is always one of the important topics in financial management; and mostly people do the research and extract sample companies based on the definition for corporate default by Taiwan Economic Journal. However, we think the timing to observe the potential corporate financial distress is extremely vital; the actual benefit will not be good even with high accuracy if relevant counterparties recognize it too late to undertake certain action for mitigating loss. The main purpose of this study is trying to alert potential corporate financial distress as early as possible, and then could contribute some to this topic. This study extracts 34 financial alerted sample companies with share prices plumped by 50% dramatically or alternatively with share prices diminished below their face value while the stock market index rose in 2007. We matched each sample company by another financially healthy company from the same industry, chose 25 financial ratios to be the variables, and running through each year by adopting logistic regression analysis. We put all variables into the regression formula and weeded out insignificant prediction variables one by one by Wald Backward Elimination, and then sieved out relatively meaningful ones. The first conclusion of this study is that we should use quarter as the financial intervals for this type of sample companies. Secondly, we found that in December and September 2007 there were three significant variables, i.e. Return on Equity (ROE), net income, operational profit ratio, inventory and account receivable to equity ratio. Thirdly, there were three significant variables in June 2007, i.e. earning before tax ratio, growth ratio of operational profit and total liability/ total equity.

This paper provides a credit risk quantification system for banks to estaminate the credit risk of loans to small and mediume nterprises(SMEs). As we know, the most difficult thing for banks to handle SME loans is whose financial reporting lacks transparency and no valuable reference. We use non-financial variables and employ the logisitic regression to develop the credit risk predict model. We concludet: first, when construct a SMEs credit rating system, non-financial factors should be seriously considered and adopted. Second, because of positioned different stage of firm life cycle, the credit rating model should be set up differently by different stage of firm. Third, SME loans should to make much of establishing ¡§relationship-based¡¨ in order to meet the various demands of risk management.

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003. / Includes bibliographical references (leaves 66-67). Also available in electronic version. Access restricted to campus users.

Thesis (Ph.D)--Industrial and Systems Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Vidakovic, Branislav; Committee Member: Edwards, Paula; Committee Member: Griffin, Paul; Committee Member: Grinter, Rebecca; Committee Member: Mullis, Rebecca. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (Ph. D.)--Florida State University, 2005. / Advisor: Dr. Fred W. Huffer, Florida State University, College of Arts and Sciences, Dept. of Statistics. Title and description from dissertation home page (viewed June 10, 2005). Document formatted into pages; contains xii, 130 pages. Includes bibliographical references.

Thesis (M.S.)--California Polytechnic State University, 2010. / Mode of access: Internet. Title from PDF title page; viewed on April 16, 2010. Major professor: Dr. Tali Freed. "Presented to the faculty of California Polytechnic State University, San Luis Obispo." "In partial fulfillment of the requirements for the degree [of] Master of Science in Industrial Engineering." "April 2010." Includes bibliographical references (p. 71).