![]() We do this by processing large amounts of unstructured log files, again with dimension reduction methods, allowing effective visualization and automatic filtering of results. In Chapter 2, we attempt to detect and predict system anomalies in large enterprise telephony systems. We develop a data driven regression model and highlight some common statistical methods that improve our predictions. Through an existing statistical arbitrage framework, we reduce the dimension of our problem with the use of correspondence analysis. stock market, where the number of stocks far exceeds the number of days relevant to the current market. In Chapter 1, we are tasked with modeling and predicting the U.S. We start with two real world problems to illustrate the practical difficulties and remedies in analyzing high dimensional data. ![]() This dissertation is on high dimensional data and their associated regularization through dimension reduction and penalization. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |