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KDD
2010
ACM
199views Data Mining» more  KDD 2010»
13 years 11 months ago
Overlapping experiment infrastructure: more, better, faster experimentation
At Google, experimentation is practically a mantra; we evaluate almost every change that potentially affects what our users experience. Such changes include not only obvious user-...
Diane Tang, Ashish Agarwal, Deirdre O'Brien, Mike ...
BMCBI
2007
120views more  BMCBI 2007»
13 years 7 months ago
Re-sampling strategy to improve the estimation of number of null hypotheses in FDR control under strong correlation structures
Background: When conducting multiple hypothesis tests, it is important to control the number of false positives, or the False Discovery Rate (FDR). However, there is a tradeoff be...
Xin Lu, David L. Perkins
AUSDM
2007
Springer
100views Data Mining» more  AUSDM 2007»
14 years 1 months ago
Predictive Model of Insolvency Risk for Australian Corporations
This paper describes the development of a predictive model for corporate insolvency risk in Australia. The model building methodology is empirical with out-ofsample future year te...
Rohan A. Baxter, Mark Gawler, Russell Ang
CSDA
2008
77views more  CSDA 2008»
13 years 7 months ago
Maximizing equity market sector predictability in a Bayesian time-varying parameter model
A large body of evidence has emerged in recent studies confirming that macroeconomic factors play an important role in determining investor risk premia and the ultimate path of eq...
Lorne D. Johnson, Georgios Sakoulis
ARTMED
1999
92views more  ARTMED 1999»
13 years 7 months ago
Two-Stage Machine Learning model for guideline development
We present a Two-Stage Machine Learning (ML) model as a data mining method to develop practice guidelines and apply it to the problem of dementia staging. Dementia staging in clin...
Subramani Mani, William Rodman Shankle, Malcolm B....