UROP Proceedings 2010-11

Department of Information Systems, Business Statistics and Operations ManagementHigh Dimensional Data AnalysisAdvisor : HU Inchi / ISOMStudent : HO Ho Yin / MAEC(UROP1100, Spring 2011)In traditional statistics, it is assumed that data contain many observations and few explanatoryvariables. High dimensional data analysis is a completely contrasting case. It is datacontaining large number of explanatory variables and limited number of observations. Atypical example would be medical test. Researchers tend to collect as much information aspossible from every test takers. As a result, they usually have only a few observations but alarge amount of explanatory variables. In this project, I study several methods for highdimensional data analysis and conduct a simulation to demonstrate these methods by usingtwo toy models.High Dimensional Data AnalysisAdvisor : HU Inchi / ISOMStudent : HU Jianchang / MAEC(UROP1100, Fall 2010)According to Donoho (2000), there are four important fields in high dimensional data analysis:Classification, clustering, regression and latent variable analysis. The ultimate goal is to try tofind out the most useful information from the huge data sets on hand and to build a modelbased on the information for predicting possible outcomes. In this project, I conduct asimulation study to test the feasibility of the method proposed by Chernoff et al (2009) basedon the backward haplotype-transmission association (BHTA) algorithm pioneered by Lo andZheng (2002). This method attempts to discover several most influential subsets of variables.The testing is also applied to an existing data set as a preparation for further study.Strategic Pricing and Timing of New ProductsAdvisor : HUI Kai Lung / ISOMStudent : LEUNG Cheuk Shing / ACCT(UROP1100, Spring 2011)This research project uses a game theory approach to investigate the problem of pricingstrategies adopted by vendors selling durable goods. Firms deal with consumers that havedifferent valuations towards a product, and so they may not buy the same product at the sameprice. Usually, firms sell projects only to those who have higher valuation, because they canachieve higher profits from such type of consumers. However, vendors will face a problemwith pricing and targeting which type of consumers when new products are introduced. In thisproject, the vendor’s timing and pricing decision are analyzed when it faces an installed baseof the old product. The setting allows the vendor to decide whether, when and how much tosell both the old and new products.64