Visualizable and interpretable regression models with good prediction power

IIE Transactions (Institute of Industrial Engineers)

Volumn 39, Issue 6, 2007, Pages 565-579

  Kim, Hyunjoong ^a   Loh, Wei Yin ^b   Shih, Yu Shan ^c   Chaudhuri, Probal ^d  

^a Yonsei University   (South Korea)

^b University of Wisconsin Madison   (United States)

^c NATIONAL CHUNG CHENG UNIVERSITY   (Taiwan)

^d INDIAN STATISTICAL INSTITUTE   (India)

Author keywords

Machine learning; Piecewise linear; Regression tree; Selection bias

Indexed keywords

GRAPH THEORY; LEAST SQUARES APPROXIMATIONS; LINEAR REGRESSION; MATHEMATICAL MODELS; PIECEWISE LINEAR TECHNIQUES; TREES (MATHEMATICS);

CONTOUR PLOT; SELECTION BIAS; TREE-STRUCTURED METHOD;

LEARNING SYSTEMS;

EID: 33947650590     PISSN: 0740817X     EISSN: 15458830     Source Type: Journal    
DOI: 10.1080/07408170600897502     Document Type: Article

References (55)

1. Aaberge, R., Colombino, U. and Strom, S. (1999) Labor supply in Italy: an empirical analysis of joint household decisions, with taxes and quantity constraints. Journal of Applied Econometrics, 14, pp. 403-422.
2. Afifi, A. and Azen, S. (1979) Statistical Analysis: A Computer Oriented Approach,, Academic Press, New York, NY
3. Belsley, D. A., Kuh, E. and Welsch, R. E. (1980) Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, Wiley, New York, NY
4. Berndt, E. R. (1991) The Practice of Econometrics, Addison-Wesley, New York, NY
5. Blake, C. and Merz, C. (1998) UCI repository of machine learning databases
6. Bollino, C. A., Perali, F. and Rossi, N. (2000) Linear household technologies. Journal of Applied Econometrics, 15, pp. 253-274.
7. Box, G. E. P. (1979) Robustness in the strategy of scientific model building. Robustness in Statistics, pp. 201-236. Academic Press, New York, NY
8. Breiman, L. and Friedman, J. (1988) Estimating optimal transformations for multiple regression and correlation. Journal of the American Statistical Association, 83, pp. 580-597.
9. Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J. (1984) Classification and Regression Trees, Wadsworth, Belmont, MA
10. Bryant, P. G. and Smith, M. A. (1996) Practical Data Analysis: Case Studies in Business Statistics, Volume 3, Irwin/McGraw Hill, Boston, MA
11. Chattopadhyay, S. (2003) Divergence in alternative Hicksian welfare measures: The case of revealed preference for public amenities. Journal of Applied Econometrics, 17, pp. 641-666.
12. Chaudhuri, P., Huang, M. C., Loh, W. Y. and Yao, R. (1994) Piecewise-polynomial regression trees. Statistica Sinica, 4, pp. 143-167.
13. Chu, S. (2001) Pricing the C's of diamond stones
14. Cochran, J. J. (2000) Career records for all modern position players eligible for the major league baseball hall of fame
15. Cochran, J. J. (2002) Data management, exploratory data analysis, and regression analysis with 1969-2000 major league baseball attendance
16. Cook, D. (1998) Regression Graphics: Ideas for Studying Regression Through Graphics, Wiley, New York
17. Cook, D. and Weisberg, S. (1994) An Introduction to Regression Graphics, Wiley, New York, NY
18. Deb, P. and Trivedi, P. K. (1997) Demand for medical care by the elderly: A finite mixture approach. Journal of Applied Econometrics, 12, pp. 313-336.
19. Delgado, M. A. and Mora, J. (1998) Testing non-nested semiparametric models: An application to Engel curves specification. Journal of Applied Econometrics, 13, pp. 145-162.
20. Denman, N. and Gregory, D. (1998) Analysis of sugar cane yields in the Mulgrave area, for the 1997 sugar cane season, Department of Mathematics, University of Queensland, Queensland, Australia
21. Fernandez, C., Ley, E. and Steel, M. F. J. (2002) Bayesian modelling of catch in a north-west Atlantic fishery. Applied Statistics, 51, pp. 257-280.
22. Friedman, J. (1991) Multivariate adaptive regression splines (with discussion). Annals of Statistics, 19, pp. 1-141.
23. Friedman, J. (2001) Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29, pp. 1189-1232.
24. Friedman, J. H. and Stuetzle, W. (1981) Projection pursuit regression. Journal of the American Statistical Association, 76, pp. 817-823.
25. Gilley, O. W. and Pace, R. K. (1996) On the Harrison and Rubinfeld data. Journal of Environmental Economics and Management, 31, pp. 403-405.
26. Hair, J. F., Anderson, R. E., Tatham, R. L. and Black, W. C. (1998) Multivariate Data Analysis, Prentice Hall, Upper Saddle River, NJ
27. Hallin, M. and Ingenbleek, J. F. (1983) The Swedish automobile portfolio in 1977: A statistical study. Scandinavian Actuarial Journal, 83, pp. 49-64.
28. Harrell Jr., F. E. (2001) Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis, Springer-Verlag, New York, NY
29. Harrison, D. and Rubinfeld, D. L. (1978) Hedonic prices and the demand for clean air. Journal of Environmental Economics and Management, 5, pp. 81-102.
30. Hastie, T. and Tibshirani, R. (1990) Generalized Additive Models, CRC Press, Boca Raton, FL
31. Hoaglin, D. C. and Velleman, P. F. (1995) A critical look at some analyses of major league baseball salaries. American Statistician, 49, pp. 277-285.
32. Horrace, W. C. and Schmidt, P. (2000) Multiple comparisons with the best, with economic applications. Journal of Applied Econometrics, 15, pp. 1-26.
33. Huber, P. J. (1981) Robust Statistics, Wiley, New York, NY
34. Kenkel, D. S. and Terza, J. V. (2001) The effect of physician advice on alcohol consumption: Count regression with an endogenous treatment effect. Journal of Applied Econometrics, 16, pp. 165-184.
35. Kooperberg, C., Bose, S. and Stone, C. (1997) Polychotomous regression. Journal of the American Statistical Association, 92, pp. 117-127.
36. Laroque, G. and Salanie, B. (2002) Labor market institutions and employment in France. Journal of Applied Econometrics, 17, pp. 25-28.
37. Liu, Z. and Stengos, T. (1999) Non-linearities in cross country growth regressions: A semiparametric approach. Journal of Applied Econometrics, 14, pp. 527-538.
38. Loh, W. Y. (2002) Regression trees with unbiased variable selection and interaction detection. Statistica Sinica, 12, pp. 361-386.
39. Lutkepohl, H., Terasvirta, T. and Wolters, J. (1999) Investigating stability and linearity of a German M1 money demand function. Journal of Applied Econometrics, 14, pp. 511-525.
40. Martins, M. F. O. (2001) Parametric and semiparametric estimation of sample selection models: An empirical application to the female labour force in Portugal. Journal of Applied Econometrics, 16, pp. 23-40.
41. Miller, A. (2002) Subset Selection in Regression,, Chapman &Hall, Boca Raton, FL
42. Neter, J., Kutner, M. H., Nachtsheim, C. J. and Wasserman, W. (1996) Applied Linear Statistical Models,, Irwin, Boston, MA
43. Olson, C. A. (1998) A comparison of parametric and semiparametric estimates of the effect of spousal health insurance coverage on weekly hours worked by wives. Journal of Applied Econometrics, 13, pp. 543-565.
44. Onoyama, K., Ohsumi, N., Mitsumochi, N. and Kishihara, T. (1998) Data analysis of deer-train collisions in eastern Hokkaido, Japan. Data Science, Classification, and Related Methods, pp. 746-751. Springer-Verlag, Tokyo, Japan
45. Pace, R. K. and Barry, R. (1997) Sparse spatial autoregressions. Statistics and Probability Letters, 33, pp. 291-297.
46. Penrose, K., Nelson, A. and Fisher, A. (1985) Generalized body composition prediction equation for men using simple measurement techniques. Medicine and Science in Sports and Exercise, 17, p. 189.
47. Quinlan, J. R. (1992) Learning with continuous classes. Proceedings of the 5th Australian Joint Conference on Artificial Intelligence World Scientific, pp. 343-348. World Scientific, Singapore
48. Rawlings, J. O. (1988) Applied Regression Analysis: A Research Tool, Wadsworth &Brooks/Cole
49. Schafgans, M. M. (1998) Ethnic wage differences in Malaysia: Parametric and semiparametric estimation of the Chinese-Malay wage gap. Journal of Applied Econometrics, 13, pp. 481-504.
50. Simonoff, J. (1996) Smoothing Methods in Statistics, Springer-Verlag, New York, NY
51. Steinberg, D. and Colla, P. (1995) CART: Tree-Structured Non-Parametric Data Analysis, Salford Systems, San Diego, CA
52. Torgo, L. (1999) Inductive learning of tree-based regression models, PhD thesis. Department of Computer Science, Faculty of Sciences, University of Porto, Porto, Portugal
53. Venables, W. N. and Ripley, B. D. (1999) Modern Applied Statistics with S-Plus, Springer-Verlag, New York, NY
54. Weiss, S. and Indurkhya, N. (1995) Rule-based machine learning methods for functional prediction. Journal of Artificial Intelligence Research, 3, pp. 383-403.
55. Witten, I. and Frank, E. (2000) Data Mining: Practical Machine Learning Tools and Techniques with JAVA Implementations, Morgan Kaufmann, San Francisco, CA