Computation of Bounds on Population Parameters When the Data Are Incomplete

Reliable Computing

Volumn 9, Issue 6, 2003, Pages 419-440

  Horowitz, Joel L ^a   Manski, Charles F ^a   Ponomareva, Maria ^a   Stoye, Jörg ^a  

^a NORTHWESTERN UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GENETIC ALGORITHMS; POPULATION STATISTICS; STATISTICAL METHODS;

POPULATION PARAMETERS;

DATA PROCESSING;

EID: 0142188223     PISSN: 13853139     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1025865520086     Document Type: Article

References (19)

1. Andrews, D. W. K.: Inconsistency of the Bootstrap When a Parameter Is on the Boundary of the Parameter Space, Econometrica 68 (2000), pp. 399-405.
2. Bickel, P. J. and Freedman, D. A.: Some Asymptotic Theory for the Bootstrap, Annals of Statistics 9 (1981), pp. 1196-1217.
3. Fischhoff, B. and Bruine de Bruin, W. Fifty/Fifty = 50%?, Journal of Behavioral Decision Making 12 (1999), pp. 149-163.
4. Heckman, J.: The Common Structure of Statistical Models of Truncation, Sample Selection, and Limited Dependent Variables and a Simple Estimator for Such Models, Annals of Economic and Social Measurement 5 (1976), pp. 479-492.
5. Horowitz, J. L. and Manski, C. F.: Censoring of Outcomes and Regressors Due to Survey Nonresponse: Identification and Estimation Using Weights and Imputations, Journal of Econometrics 84 (1998), pp. 37-58.
6. Horowitz, J. L. and Manski, C. F.: Imprecise Identification from Incomplete Data, in: Proceedings of the Second International Symposium on Imprecise Probabilities and Their Applications, 2001, http://ippserv.rug.ac.be/̃isipta01/proceedings/index.html.
7. Horowitz, J. L. and Manski, C. F.: Nonparametric Analysis of Randomized Experiments with Missing Covariate and Outcome Data, Journal of the American Statistical Association 95 (2000), pp. 77-84.
8. Imbens, G. and Manski, C.: Confidence Intervals for Partially Identified Parameters, Department of Economics, University of California at Berkeley, 2003, in process.
9. Judd, K. L.: Numerical Methods in Economics, The MIT Press, Cambridge, 1998.
10. Manski, C. F.: Identification Problems in the Social Sciences, Harvard University Press, Cambridge, 1995.
11. Manski, C. F. and Straub, J.: Worker Perceptions of Job Insecurity in the Mid-1990s: Evidence from the Survey of Economic Expectations, Journal of Human Resources 35 (2000), pp. 447-479.
12. Manski, C. F. and Tamer, E.: Inference on Regressions with Interval Data on a Regressor or Outcome, Econometrica 70 (2002), pp. 519-546.
13. Myrveit, I., Stensrud, E., and Olsson, U.: Analysing Data Sets with Missing Data: An Empirical Evaluation of Imputation Methods and Likelihood-Based Methods, IEEE Transactions on Software Engineering 27 (2001), pp. 999-1013.
14. Politis, D. N. and Romano, J. P.: Large Sample Confidence Regions Based on Subsamples under Minimal Assumptions, Annals of Statistics 22 (1994), pp. 2031-2050.
15. Rosenbaum, P.: Observational Studies, Springer-Verlag, New York, 1995.
16. Rubin, D.: Inference and Missing Data, Biometrika 63 (1976), pp. 581-590.
17. Scharfstein, D., Rotnitzky, A., and Robins, J.: Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models, Journal of the American Statistical Association 94 (1999), pp. 1096-1120.
18. Vansteelandt, S. and Goetghebeur, E.: Analyzing the Sensitivity of Generalized Linear Models to Incomplete Outcomes Via the IDE Algorithm, Journal of Computational and Graphical Statistics 10 (2001), pp. 656-672.
19. Zaffalon, M.: Exact Credal Treatment of Missing Data, Journal of Statistical Planning and Inference 105 (2002), pp. 105-122.