Computer-intensive methods in the social sciences

Social Science Computer Review

Volumn 15, Issue 1, 1997, Pages 83-97

  Cirincione, Carmen ^a   Gurrieri, Gustavo A ^a  

^a UNIVERSITY OF CONNECTICUT   (United States)

Author keywords

Bootstrapping; Computer intensive; Cross validation; Jackknife; Monte Carlo simulation; Permutation tests; Randomization tests; Resampling

Indexed keywords

EID: 21444434059     PISSN: 08944393     EISSN: None     Source Type: Journal    
DOI: 10.1177/089443939701500108     Document Type: Article

References (31)

1. Aczel, A. D., & Josephy, N. H. (1992). Using the bootstrap for improved ARIMA model identification. Journal of Forecasting, 11, 71-80.
2. Barnes, G. M., Weite, J. W., & Dintcheff, B. (1991). Drinking among subgroups in the adult population of New York State: A classification analysis using CART. Journal of Studies on Alcohol, 52, 338-344.
3. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Pacific Grove, CA: Wadsworth & Brooks/Cole Advanced Books & Software.
4. Campbell, B., & Ghysels, E. (1995). Federal budget projections: A nonparametric assessment of bias and efficiency. Review of Economics and Statistics, 77, 17-31.
5. Chant, D., & Dagleish, L. (1992). A SAS macro for jackknifing the results of discriminant analysis. Multivariate Behavioral Research, 27, 323-333.
6. Chen, W. J., Faraone, S. V., Biederman, J., & Tsuang, M. T. (1994). Diagnostic accuracy of the Child Behavior Checklist scales for attention-deficit disorder: A receiver-operating characteristic analysis. Journal of Consulting and Clinical Psychology, 62, 1017-1025.
7. Chung, J. H., & Fraser, D.A.S. (1958). Randomization tests for a multivariate two-sample problem. Journal of the American Statistical Association, 53, 729-735.
8. Cirincione, C., Darling, T., & O'Rourke, T. (1996, August). Race and congressional redistricting: A computer-intensive evaluation of the 1992 North Carolina plan. Paper presented at the annual meeting of the American Political Science Association, San Francisco.
9. Crowley, P. H. (1992). Resampling methods for computation-intensive data analysis in ecology and evolution. In D. G. Fautin, D. J. Futuyma, & F. C. James (Eds.), Annual review of ecology and systematics (Vol. 23, pp. 405-448). Palo Alto, CA: Annual Reviews, Inc.
10. Dixon, P. M. (1993). The bootstrap and the jackknife: Describing the precision of ecological indices. In S. M. Scheiner & J. Gurevitch (Eds.), Design and analysis of ecological experiments (pp. 290-318). London: Chapman & Hall.
11. Dwass, M. (1957). Modified randomization tests for nonparametric hypotheses. Annals of Mathematical Statistics, 28, 181-187.
12. Edgington, E. (1995). Randomization tests. New York, Marcel Dekker.
13. Efron, B. (1982). The jackknife, the bootstrap, and other resampling plans. Philadelphia: Society for Industrial and Applied Mathematics.
14. Efron, B., & Tibshirani, R. (1993). Introduction to the bootstrap. London: Chapman & Hall.
15. Engstrom, R. L., & Wildgen, J. K. (1977). Pruning thorns from the thicket: An empirical test of the existence of racial gerrymandering. Legislative Studies Quarterly, 11, 465-478.
16. Ferron, J., & Ware, W. (1994). Using randomization tests with responsive single-case designs. Behavior Research and Therapy, 32, 787-791.
17. Ferron, J., & Ware, W. (1995). Using randomization tests with responsive single-case designs: Erratum. Behavior Research and Therapy, 33, 241.
18. Fisher, R. A. (1935). Design of experiments. Edinburgh, Scotland: Oliver & Boyd.
19. Good, P. (1994). Permutation tests: A practical guide to resampling methods for testing hypotheses. New York: Springer-Verlag.
20. Goolkasian, P. (1996). Testimate. Social Science Computer Review, 14, 230-231.
21. Gustman, A. L., & Steinmeier, T. L. (1991). Changing the social security rules for work after 65. Industrial and Labor Relations Review, 44, 733-745.
22. Hjoth, J.S.U. (1994). Computer intensive statistical methods. London: Chapman & Hall.
23. Horowitz, J. K., & Carson, R. T. (1991). A classification tree for predicting consumer preferences for risk reduction. American Journal of Agricultural Economics, 73, 1416-1421.
24. Kallam, M. (1994). Testimate. Social Science Computer Review, 12, 149-152.
25. Kempthorne, O. (1986). Randomization II. In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of statistical sciences (Vol. 7, pp. 519-524). New York: John Wiley.
26. Kempthorne, O., & Doerfler, T. E. (1969). The behaviour of some significance tests under experimental randomization. Biometrika, 56, 231-248.
27. Kling, C. L., & Sexton, R. J. (1990). Bootstrapping in applied welfare analysis. American Journal of Agricultural Economics, 72, 406-418.
28. Kromrey, J. D., & Hines, C. V. (1995). Use of empirical estimates of shrinkage in multiple regression: A caution. Educational and Psychological Measurement, 55, 901-925.
29. LePage, R., & Billard, L. (1992). Exploring the limits of bootstrap. New York: John Wiley.
30. Manly, B. (1992). Randomization and Monte Carlo methods in biology, London: Chapman & Hall.
31. Manly, B.FJ. (1995a). On comparing sample means by randomization, with unequal variances. Paper presented at A. C. Aitken Centenary Conference, Dunedin, New Zealand.