Data Mining and Business Analytics with R

Data Mining and Business Analytics with R

Volumn , Issue , 2013, Pages

  Ledolter, Johannes ^a  

^a UNIVERSITY OF IOWA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84891583919     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9781118596289     Document Type: Book

References (60)

1. Ledolter, J. and Burrill, C.: Statistical Quality Control: Strategies and Tools for Continual Improvement. New York: John Wiley & Sons, Inc., 1999.
2. Adler, J.: R In a Nutshell: A Desktop Quick Reference. Sebastopol, CA: O'Reilly Media, Inc., 2009.
3. Ledolter, J. and Swersey, A.: Testing 1-2-3: Experimental Design with Applications in Marketing and Service Operations. Stanford, CA: Stanford University Press, 2007.
4. Montgomery, A.L.: Creating micro-marketing pricing strategies using supermarket scanner data. Marketing Science, Vol. 16 (1987), 315-337.
5. Abraham, B. and Ledolter, J.: Introduction to Regression Modeling. Belmont, CA: Duxbury Press, 2006.
6. Hawkins, D.M.: The problem of overfitting. Journal of Chemical Information and Computer Science, Vol. 44 (2004), 1-12.
7. Ledolter, J. and Abraham, B.: Parsimony and its importance in time series forecasting. Technometrics, Vol. 23 (1981), 411-414.
8. Shmueli, G., Patel, N.R., and Bruce, P.C.: Data Mining for Business Intelligence. Second edition. Hoboken, NJ: John Wiley & Sons, Inc., 2010.
9. Brinkman, N.D.: Ethanol fuel-a single-cylinder engine study of efficiency and exhaust emissions. SAE Transactions, Vol. 90 (1981).
10. Cleveland, W.S. and Devlin, S.J.: Locally weighted regression: an approach to regression analysis by local fitting. Journal of the American Statistical Association, Vol. 83 (1988), 596-610.
11. Craven, P. and Wahba, G.: Smoothing noisy data with spline functions. Numerische Mathematik, Vol. 31 (1979), 377-403.
12. Epanechnikov, V.A.: Nonparametric estimates of a multivariate probability density. Theory of Probability and its Applications, Vol. 14 (1969), 153-158.
13. Fan, J. and Gijbels, I.: Local Polynomial Modelling and Its Applications. London: Chapman and Hall, 1996.
14. Loader, C.: Local Regression and Likelihood. New York: Springer, 1999.
15. Benjamini, Y. and Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B, Vol. 57 (1995), No 1, 289-300.
16. Benjamini, Y. and Yekutieli, D.: The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, Vol. 29 (2001), No 4, 1165-1188.
17. Efron, B., Johnstone, I., Hastie, T., and Tibshirani, R.: Least angle regression. Annals of Statistics, Vol. 32 (2004), No 2, 407-499.
18. Hastie, T., Tibshirani, R., and Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference and Prediction. Second edition. New York: Springer, 2009.
19. Montgomery, A.L.: Creating micro-marketing pricing strategies using supermarket scanner data. Marketing Science, Vol. 16 (1987), 315-337.
20. Stamey, T., Kabalin, J., McNeal, J., Johnstone, I., Freiha, F., Redwine, E., and Yang, N.: Prostate specific antigen in the diagnosis and treatment of adenocarcinoma of the prostate, ii: radical prostatectomy treated patients. Journal of Urology, Vol. 141 (1989), 1076-1083.
21. Tibshirani, R.: Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society: Series B, Vol. 58 (1996), No 1, 267-288.
22. Abraham, B. and Ledolter, J.: Introduction to Regression Modeling. Belmont, CA: Duxbury Press, 2006.
23. Asuncion, A. and Newman, D.J.: UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science, 2007. Available at http://www.ics.uci.edu/~mlearn/MLRepository.html. Accessed 2013 Jan 10.
24. Shmueli G., Patel, N.R., and Bruce, P.C.: Data Mining for Business Intelligence. Second edition. Hoboken, NJ: John Wiley & Sons, Inc., 2010.
25. Asuncion, A. and Newman, D.J.: UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science, 2007. Available at http://www.ics.uci.edu/~mlearn/MLRepository.html. Accessed 2013 Jan 17.
26. Shmueli G., Patel, N.R., and Bruce, P.C.: Data Mining for Business Intelligence. Second edition. Hoboken, NJ: John Wiley & Sons, Inc., 2010.
27. Agresti, A.: Categorical Data Analysis. Second edition. New York: John Wiley & Sons, Inc., 2002.
28. Abraham, B. and Ledolter, J.: Introduction to Regression Modeling. Belmont, CA: Duxbury Press, 2006 (Chapter 11).
29. Taddy, M.: Multinomial inverse regression for text analysis. 2012. Available at http://arxiv.org/abs/1012.2098. Accessed 2013 Feb 10. To appear in Journal of the American Statistical Association, Vol. 108 (2013).
30. Johnson, R.A. and Wichern, D.W.: Applied Multivariate Statistical Analysis. Second edition. Englewood Cliffs, NJ: Prentice Hall, 1988.
31. Biggs, D., DeVille, B., and Suen, E.: A method of choosing multiway partitions for classification and decision trees. Journal of Applied Statistics, Vol. 18 (1991), 49-62.
32. Breiman, L., Friedman, J., Ohlsen, R., and Stone, C.: Classification and Regression Trees. Pacific Grove: Wadsworth, 1984.
33. Granger, C.W.J and Bates, J.: The combination of forecasts. Operations Research Quarterly, Vol. 20 (1969), 451-468.
34. Karatzoglou, A., Meyer, D., and Hornik, K: Support vector machines in R. Journal of Statistical Software, Vol. 15 (2006), No 1, 1-28.
35. Kass, G.V.: An exploratory technique for investigating large quantities of categorical data. Applied Statistics, Vol. 29 (1980), 119-127.
36. Williams, G.: Data Mining with Rattle and R: the Art of Excavating Data for Knowledge Discovery. New York: Springer, 2011.
37. Hand, D.J., Daly, F., Lunn, A.D., McConway, K.J., and Ostrowski, E.: A Handbook of Small Data Sets. London: Chapman & Hall, 1994.
38. Hartigan, J.A.: Clustering Algorithms. New York: John Wiley & Sons, Inc., 1975.
39. Hartigan, J.A. and Wong, M.A.: A k-means clustering algorithm. Applied Statistics, Vol. 28 (1979), 100-108.
40. Weber, A.: Agrarpolitik im Spannungsfeld der Internationalen Ernaehrungspolitik. Kiel: Institut fuer Agrarpolitik und Marktlehre, 1973.
41. Celma, O.: Music Recommendation and Discovery. New York: Springer, 2010.
42. Hahsler, M., Gruen, B., and Hornik, K.: arules-A computational environment for mining association rules and frequent item sets. Journal of Statistical Software, Vol. 14 (2005), 1-25.
43. Abraham, B. and Ledolter, J.: Statistical Methods for Forecasting. New York: John Wiley & Sons, Inc., 1983.
44. Box, G.E.P., Jenkins, G.M., and Reinsel, G.C.: Time Series Analysis, Forecasting and Control. Third edition. Englewood Cliffs: Prentice Hall, 1994.
45. Frank, I.E. and Friedman, J.H.: A statistical view of some chemometrics regression tools. Technometrics, Vol. 35 (1993), 109-135.
46. Hastie, T., Tibshirani, R., and Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference and Prediction. Second edition. New York: Springer, 2009.
47. Johnson, R.A. and Wichern, D.W.: Applied Multivariate Statistical Analysis. Second edition. Englewood Cliffs, NJ: Prentice Hall, 1988.
48. Stock, J.H. and Watson, M.W.: Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association, Vol. 97 (2002a), 1167-1179.
49. Stock, J.H. and Watson, M.W.: Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics, Vol. 20 (2002b), 147-162.
50. Wold, H.: Soft modeling by latent variables: The nonlinear iterative partial least squares approach. In Perspectives in Probability and Statistics, Papers in Honour of M.S. Bartlett; Gani, J., Ed.;. Sheffield: Applied Probability Trust, 1975.
51. Gentzkow, M. and Shapiro, J.: What drives media slant? Evidence from U.S. daily newspapers. Econometrica, Vol. 78 (2010), 35-71.
52. Taddy, M.: Multinomial inverse regression for text analysis. 2012a. Available at http://arxiv.org/abs/1012.2098. Accessed 2013 Jan 18. To appear in Journal of American Statistical Association, Vol. 108 (2013).
53. Taddy, M.: textir (R package), 2012b.
54. Breiger, R. and Pattison, P.: Cumulated social roles: The duality of persons and their algebras. Social Networks, Vol. 8 (1986), 215-256.
55. Burt, R.S.: Structural holes and good ideas. American Journal of Sociology, Vol. 110 (2004), No 2, 349-399.
56. Handcock, M.S., Hunter, D.R., Butts, C.T., Goodreau, S.M., and Morris, M.: Statnet: Software tools for the representation, visualization, analysis and simulation of network data. Journal of Statistical Software, Vol. 24 (2008), No. 1, 1-11.
57. Padgett, J.F.: Marriage and elite structure in Renaissance Florence, 1282-1500. Paper delivered to the Social Science History Association, 1994. Available at http://www4.ncsu.edu/~gremaud/MA432/padgett.pdf.
58. Padgett, J.F. and Ansel, C.K.: Robust action and rise of the Medici 1400-1434. American Journal of Sociology, Vol. 98 (1993), 1259-1319.
59. Resnick, M.D., Bearman, P.S., Blum, R.W., Bauman, K.E., Harris, K.M., Jones, J., Tabor, J., Beuhring, T., Sieving, R.E., Shew, M., Ireland, M., Bearinger, L.H., and Udry, J.R.: Protecting adolescents from harm. Findings from the National Longitudinal Study on Adolescent Health. Journal of the American Medical Association, Vol. 278 (1997), 823-832.
60. Wasserman, S. and Faust, K.: Social Network Analysis: Methods and Applications. Cambridge, England: Cambridge University Press, 1994.