(1 + ε)-approximate sparse recovery
Eric Price, David P. Woodruff
FOCS 2011
In this paper we present a method for drawing inferences about the process of financial losses that are associated with the operations of a business. For example, for a bank such losses may be related to erroneous transactions, human error, fraud, lawsuits, or power outages. Information about the frequency and magnitude of losses is obtained through the search of a number of sources, such as printed, computerized, or Internet-based publications related to insurance and finance. The data consists of losses that were discovered in the search. We assume that the probability of a loss appearing in the body of sources and also being discovered increases with the magnitude of the loss. Our approach simultaneously models the process of losses and the process of populating the database. The approach is illustrated using data related to operational risk losses that are of special interest to the banking industry. © 2007 IBM.
Eric Price, David P. Woodruff
FOCS 2011
Chi-Leung Wong, Zehra Sura, et al.
I-SPAN 2002
Lerong Cheng, Jinjun Xiong, et al.
ASP-DAC 2008
Michael Ray, Yves C. Martin
Proceedings of SPIE - The International Society for Optical Engineering