Olshen's research is in statistics and their applications to medicine and biology. Many efforts have concerned tree-structured algorithms for classification, regression, survival analysis, and clustering. Those for classification have been used with success in computer-aided diagnosis and prognosis, while those for clustering have been applied to lossy data compression in digital radiography. Modeling and sample reuse methods have been developed for longitudinal data, concerning gait analysis; renal physiology; cholesterol; nephrophysiology; and recently, molecular genetics.
Last modified Wed, 25 Jul, 2012 at 11:07
|Termination and continuity of greedy growing for tree structured vector quantizers||Olshen RA.; Nobel AB||IEEE Transactions on Information Theory||01-1996|
|Predicting high-risk cholesterol levels||Olshen RA; Garber AM; Zhang H; Venkatraman ES||Intl. Stat. Rev.||01-1994|
|Evaluating quality of compressed medical images: SNR, subjective rating, and diagnostic accuracy||Olshen RA.; Cosman PC; Gray RM||Proceedings of the IEEE||01-1994|
|Relationships among protein and albumin concentrations and oncotic pressure in nephrotic plasma||Canaan-KÃ¼hl S; Venkatraman ES; Ernst SI; Olshen RA; Myers BD||Am J Physiol||01-1993|
|Classification and Regression Trees||Olshen RA; Breiman L; Friedman JH; Stone CJ||01-1984|
Institute of Mathematical Statistics, American Statistical Association American Association for the Advancement of Science Institute of Electrical and Electronics Engineers