Parametric and semiparametric model-based estimates of the finite population mean for two-stage cluster samples with item nonresponse

Biometrics

Volumn 63, Issue 4, 2007, Pages 1172-1180

  Yuan, Ying ^a   Little, Roderick J A ^b  

^a UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

^b UNIVERSITY OF MICHIGAN SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Cluster specific nonignorable nonresponse; Item nonresponse; Outcome specific nonignorable nonresponse; Penalized spline of propensity prediction; Two stage cluster sample

Indexed keywords

BIOMETRICS;

CLUSTER-SPECIFIC NONIGNORABLE NONRESPONSE; COVARIATES; ITEM NONRESPONSE; NONIGNORABLE NONRESPONSE; OUTCOME-SPECIFIC NONIGNORABLE NONRESPONSE; PARAMETRIC MODELS; PENALIZED SPLINE OF PROPENSITY PREDICTION; PENALIZED SPLINES; TWO-STAGE CLUSTER SAMPLE;

RANDOM PROCESSES;

CLUSTER ANALYSIS; COVARIANCE ANALYSIS; POPULATION MODELING;

ANALYTIC METHOD; ARTICLE; CLUSTER ANALYSIS; COMPARATIVE STUDY; DATA ANALYSIS; HEALTH SURVEY; MATHEMATICAL MODEL; PARAMETRIC TEST; SCORING SYSTEM; SIMULATION;

EID: 36749072707     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2007.00816.x     Document Type: Article

References (22)

1. Cohen, G. and Duffy, J. C. (2002). Are nonrespondents to health surveys less healthy than respondents. Journal of Official Statistics 18, 13 23.
2. Gelfand, A. E., Hills, S. E., Racine-Poon, A., and Smith, A. F. M. (1990). Illustration of Bayesian inference in normal data models using Gibbs sampling. Journal of the American Statistical Association 85, 972 985.
3. Gelman, A. and Rubin, D. (1992). Inference from iterative simulation using multiple sequences. Statistical Science 7, 457 511.
4. Groves, R. M. and Couper, M. P. (1998). Nonresponse in Household Interview in Surveys. New York : Wiley.
5. Hastie, T. J. and Tibshirani, R. J. (1990). Generalized Additive Models. New York : Chapman & Hall.
6. Horvitz, D. G. and Thompson, D. J. (1952). A generalization of sampling without replacement from a finite population. Journal of the American Statistical Association 47, 663 685.
7. Little, R. J. A. (2003). The Bayesian approach to sample survey inference. In Analysis of Survey Data, R. L. Chambers and C. J. Skinner (eds 49 57. New York : John Wiley & Sons.
8. Little, R. J. A. (2004). To model or not to model? Competing models of inference for finite population sampling. Journal of the American Statistical Association 99, 546 556.
9. Little, R. J. A. and An, H. (2004). Robust likelihood-based analysis of multivariate data with missing values. Statistica Sinica 14, 949 968.
10. Little, R. J. A. and Rubin, D. B. (2002). Statistical Analysis with Missing Data, 2nd edition. New York : John Wiley & Sons.
11. Nandram, B. and Choi, J. W. (2005). Hierarchical Bayesian nonignorable regression models for small areas: An application to the NHANES data. Survey Methodology 31, 73 84.
12. Nandram, B., Han, G., and Choi, J. W. (2002). A hierarchical Bayesian nonignorable nonresponse model for multinomial data from small areas. Survey Methodology 28, 145 156.
13. National Center for Health Statistics 1994). Plan and operation of the third national health and nutrition examination survey. Vital and Health Statistics Series 1, 32.
14. National Center for Health Statistics 2006). Available at http://www.cdc.gov/nchs/nhanes.htm.
15. Ogden, C. L., Flegal, K. M., Carroll, M. D., and Johnson, C. L. (2002). Prevalence and trends in overweight among US children and adolescents. Journal of the American Medical Association 288, 1728 1732.
16. Rosenbaum, P. R. and Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41 55.
17. Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. New York : John Wiley & Sons.
18. Ruppert, D., Wand, M. P., and Carroll, R. J. (2003). Semiparametric regression. Cambridge, U.K. : Cambridge University Press.
19. Scott, A. and Smith, T. M. F. (1969). Estimation in multi-stage surveys. Journal of the American Statistical Association 64, 830 840.
20. Yuan, Y. and Little, R. J. A. (2007). Model-based estimates of the finite population mean for two-stage cluster samples with unit non-response. Applied Statistics 56, 79 97.
21. Zhang, G. and Little, R. J. (2005). Extensions of the penalized spline propensity prediction method of imputation. American Statistical Association, Proceedings of the Joint Statistical Meetings.
22. Zheng, H. and Little, R. J. A. (2004). Penalized spline nonparametric mixed models for inference about a finite mean from two-stage samples. Survey Methodology 30, 209 218.