Here's a first draft of the abstract for this term's paper. Google's dictionary didn't recognize "heteroscedasticity" and wanted to change it to "heterosexuality". I don't need a spell checker making that mistake! I added it to my dictionary.
Financial Service Companies are now seeing data warehouse volumes running into the
trillions of rows. Under such volumes, performance of queries using conventional
“full search” technology (e.g., SQL, OLAP cubes) can degrade to where the user
experience of immediate feedback is compromised. Simple random sampling methods
do not yield reliable results due to the typically heavy-tailed distributions
of financial data and heteroscedasticity. In this paper we present an algorithm
that adjusts for both factors and returns both a point estimate and a
confidence interval for the queried result. Empirical tests from a sample of
projected cash flows are given to demonstrate the convergence and correctness
of the results.
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