Dimension reduction summaries for balanced contrasts

Journal of Statistical Planning and Inference

Volumn 139, Issue 2, 2009, Pages 617-628

  Nelson, David ^a   Noorbaloochi, Siamak ^a  

^a UNIVERSITY OF MINNESOTA   (United States)

Author keywords

Conditional density ratios; Dimension reduction; Propensity; Sufficient summary

Indexed keywords

EID: 55149094312     PISSN: 03783758     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jspi.2008.04.031     Document Type: Article

References (22)

1. Cochran W.G. Analysis of covariance: its nature and uses. Biometrics 13 (1957) 261-281
2. Cochran W.G. The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics 24 (1968) 295-313
3. Cochran W.G. The use of covariance in observational studies. Appl. Statist. 18 (1969) 270-275
4. Cochran W.G., and Chambers S.P. The planning of observational studies of human populations. J. Roy. Statist. Soc., Ser. A 128 (1965) 234-266
5. Cook R.D. Regression graphics: ideas for studying regressions through graphics (1998), Wiley, New York
6. Cook R.D., and Weisberg S. Discussion of Li (1991). J. Amer. Statist. Assoc. 86 (1991) 328-332
7. Cook R.D., and Weisberg S. Applied Regression Including Computing and Graphics (1999), Wiley, New York
8. Cox T.F., and Ferry G. Robust logistic discrimination. Biometrika 78 (1999) 841-849
9. Drake C. Effects of misspecification of the propensity score on estimators of treatment effect. Biometrics 49 (1993) 1231-1236
10. Erosheva E.A., Feinberg S.E., and Junker B.W. Alternative statistical methods and representations for large sparse multi-dimensional contingency tables. Ann. Faculté Sci. Toulouse, Math. 11 (2002) 485-505
11. Fisher R.A. Statistical Methods and Scientific Inference (1973), Hafner Press, New York
12. Greenland S., Robins J., and Pearl J. Confounding and collapsibility in causal inference. Statist. Sci. 14 (1999) 29-46
13. Imai K., and van Dyk D.A. Causal inference with general treatment regimes: generalizing the propensity score. J. Amer. Statist. Assoc. 99 (2004) 854-866
14. Imbens G.W. The role of the propensity score in estimating dose-response functions. Biometrika 87 (2000) 706-710
15. Lechner, M., 1999. Identification and estimation of causal effects of multiple treatments under the conditional independence assumption, IZA Discussion Papers 91, Institute for the Study of Labor (IZA).
16. Li K.C. Sliced inverse regression for dimension reduction (with discussion). J. Amer. Statist. Assoc. 86 (1991) 316-342
17. Li K.C. On principal Hessian directions for data visualization and dimension reduction: another application of Stein's lemma. J. Amer. Statist. Assoc. 87 (1992) 1025-1039
18. Noorbaloochi, S., Nelson, D., 2008. Conditionally specified models and dimension reduction in the exponential families. J. Multivariate Anal., in press, doi:10.1016/j.jmva.2007.10.001.
19. Qin J. Inferences for case-control and semiparametric two-sample density ratio models. Biometrika 85 (1998) 619-630
20. Rosenbaum P.R., and Rubin D.B. The central role of the propensity score in observational studies for causal effects. Biometrika 70 (1983) 41-55
21. Rubins H.B., Nelson D., Noorbaloochi S., and Nugent S. Effectiveness of lipid-lowering medications in outpatients with coronary heart disease in the Department of Veterans Affairs System. Amer. J. Cardiol. 92 (2003) 1177-1182
22. Simonoff J.S. Smoothing categorical data. J. Statist. Plann. Inference 47 (1995) 41-69 Biometrika 70, 41-55