Recently, several spectacular bankruptcies, including Fannie Mae, Freddie Mac, Washington Mutual, Merrill Lynch, and Lehman Brothers, have caught the world by surprise. To improve the accuracy of financial distress predictions, this research compares traditional statistical methods (i.e., linear discriminant analysis, logistic regression), decision tree classification methods (i.e., C5.0, CART,...

In previous work it was found that cache blocking of sparse matrix vector multiplication yielded significant performance improvements (upto 700% on some matrix and platform combinations) however deciding when to apply the optimization is a non-trivial problem. This paper applies four different statistical learning techniques to explore this classification problem. The statistical techniques use...

We present a new simulation paradigm for microarchitectural design evaluation and optimization. This paradigm counters increasing simulation costs attributed to the exponentially increasing size of design spaces and the need for more thorough, comprehensive studies when evaluating increasingly diverse design options. We present a tutorial for (1) obtaining a more comprehensive understanding of ...

https://rde.ac Logistic regression is a regression model where the dependent variable is categorical and corresponding independent variables can be categorical or continuous. This article covers the case of a binary dependent variable such as an event occurring coded 1 = ‘event’ and 0 = ‘no event’. Frequent outcomes are pass/fail, win/lose, disease/no disease, etc. The logistic regression model...

Statistical graphics play a crucial role in exploratory data analysis, model checking and diagnosis. Until recently there were no formal visual methods in place for determining statistical significance of findings. This changed, when Buja et al. [2009] conceptually introduced two protocols for formal tests of visual findings. In this paper we take this a step further by comparing the lineup pro...