CHANCE

, 22:58

Will they stay or will they go?

Predicting disenrollment from home care services to nursing homes using classification trees
  • Douglas A. Noe
  • Ian M. Nelson
  • Shahla Mehdizadeh
  • A. John Bailer
Columns Here’s to Your Health

Further Reading

  1. Berlowitz, D.R., A.S. Ash, E.C. Hickey, R.H. Friedman, M. Glickman, B. Kader, and M.A. Moskowitz. 1998. Inadequate management of blood pressure in a hypertensive population. New England Journal of Medicine 339:1957–1963.CrossRefGoogle Scholar
  2. Breiman, L. 2001. Random forests. Machine Learning 45(1): 5–32.MATHCrossRefGoogle Scholar
  3. Breiman, L., J.H. Friedman, R.A. Olshen, and C.J. Stone. 1984. Classification and regression trees. New York: Chapman & Hall.MATHGoogle Scholar
  4. Chipman, H., E. George, and R. McCulloch. 2008. BART: Bayesian additive regression trees. http://arxiv.org/abs/0806.3286.
  5. Loh, W.Y. 2002. Regression trees with unbiased variable selection and interaction detection. Statistica Sinica 12:361–386.MATHMathSciNetGoogle Scholar
  6. Mehdizadeh S., I. Nelson, and L. Theimen. 2007. PASSPORT consumer eligibility. Miami, Ohio: Miami University. www.scripps.muohio.edu/research/publications/documents/PASSPORTConsumerEligibilityv1.pdf.Google Scholar
  7. Toschke, A.M., A. Beyerlein, and R. von Kries. 2005. Children at high risk for overweight: A classification and regression trees analysis approach. Obesity Research 13:1270–1274.CrossRefGoogle Scholar

Copyright information

© American Statistical Association 2009

Authors and Affiliations

  • Douglas A. Noe
  • Ian M. Nelson
  • Shahla Mehdizadeh
  • A. John Bailer

There are no affiliations available

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