A kernel smoothing method of adjusting for unit non-response in sample surveys

Canadian Journal of Statistics

Volumn 34, Issue 4, 2006, Pages 553-579

  Da Silva, Damião N ^a   Opsomer, Jean D ^b  

^a FEDERAL UNIVERSITY OF RIO GRANDE DO NORTE   (Brazil)

^b Iowa State University   (United States)

Author keywords

Jackknife; Kernel regression; Missing data; Propensity score; Response probability; Sampling survey; Weighting adjustment

Indexed keywords

EID: 33847713662     PISSN: 03195724     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (29)

1. F. J. Breidt & J. D. Opsomer (2000). Local polynomial regression estimators in survey sampling. The Annals of Statistics, 28, 1026-1053.
2. C.-M. Cassel, C.-E. Särndal & J. H. Wretman ( 1983). Some uses of statistical models in connection with the non-response problem. In Incomplete Data in Sample Surveys, Volume 3: Theory and Bibliographies (W. G. Madow, I. Olkin & D. B. Rubin, eds.), pp. Academic Press, New York, 143-160.
3. Y. P. Chaubey & A. N. Crisalli (1995). Adjustment of the inclusion probabilities in case of non-response. In Proceedings of the Survey Methods Section: Papers Presented at the 23rd Annual Meeting of the Statistical Society of Canada, Montréal, Québec, Canada, July 10-13, 1995, Statistical Society of Canada, pp. 75-79.
4. D. N. Da Silva (2003). Adjustments for Survey Unit Non-response under Nonparametric Response Mechanisms. Doctoral dissertation, Iowa State University, Ames.
5. D. N. Da Silva & J. D. Opsomer (2004). Properties of the weighting cell estimator under a nonparametric response mechanism. Survey Methodology, 30, pp. 45-55.
6. M. H. David, R. Little, M. Samuhel & R. Triest (1983). Imputation models based on the propensity to respond. In ASA Proceedings of the Business and Economic Statistics Section, 168-173.
7. J. Fan (1993). Local linear regression smoothers and their minimax efficiencies. The Annals of Statistics, 21, 196-216.
8. R. E. Fay (1991). A design-based perspective on missing data variance. In Proceedings of the Bureau of the Census Annual Research Conference, U.S. Bureau of the Census, pp. 429-440.
9. W. A. Fuller & J.-K. Kim (2005). Hot deck imputation for the response model. Survey Methodology, 31, 139-149.
10. A. Giommi (1984). A simple method for estimating individual response probabilities in sampling from finite populations. Metron, 42, 185-200.
11. A. Giommi (1987). Nonparametric methods for estimating individual response probabilities. Survey Methodology, 13, 127-134.
12. C. Isaki & W. Fuller (1982). Survey design under the regression superpopulation model. Journal of the American Statistical Association, 77, 89-96.
13. G. Kalton & D. Kasprzyk (1986). The treatment of missing survey data. Survey Methodology, 12, 1-16.
14. G. Kalton & D. S. Maligalig (1991). A comparison of methods of weighting adjustment for non-response. In Proceedings of the Bureau of the Census Annual Research Conference, U.S. Bureau of the Census, pp. 409-428.
15. R. J. A. Little (1986). Survey non-response adjustments for estimates of means. International Statistical Review, 54, 139-157.
16. R. J. A. Little & D. B. Rubin (2002). Statistical Analysis with Missing Data. Wiley, New York.
17. M. Nargundkar & G. B. Joshi (1975). Non-response in sample surveys. In Proceedings of the 40th Session of the International Statistical Institute (Warsaw, 1975), Volume 3: Contributed Papers = Bulletin de l'Institut International de Statistique, 46 (3), 626-628.
18. T. Niyonsenga (1994). Nonparametric estimation of response probabilities in sampling theory. Survey Methodology, 20, 177-184.
19. T. Niyonsenga (1997). Response probability estimation. Journal of Statistical Planning and Inference, 59, 111-126.
20. H. L. Oh & F. J. Scheuren (1983). Weighting adjustments for unit non-response. In Incomplete Data in Sample Surveys, Volume 2: Theory and Bibliographies (W. G. Madow, I. Olkin & D. B. Rubin, eds.), Academic Press, New York, pp. 143-184.
21. R Development Core Team (2004). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna.
22. P. R. Rosenbaum & D. B. Rubin (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41-55.
23. P. R. Rosenbaum & D. B. Rubin (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician, 39, 33-38.
24. C. E. Särndal & B. Swensson (1985). Incorporating non-response modelling in a general randomization theory approach. In Proceedings of the 45th Session of the International Statistical Institute: Volume 15.2, pp. 1-15. International Statistical Institute, Voorburg, The Netherlands.
25. C. E. Särndal & B. Swensson (1987). A general view of estimation for two phases of selection with applications to two-phase sampling and non-response. International Statistical Review, 55, 279-294.
26. P. K. Sen & J. d. M. Singer (1993). Large Sample Methods in Statistics: An Introduction with Applications. Chapman & Hall Ltd.
27. J. Shao & P. Steel (1999). Variance estimation for survey data with composite imputation and nonnegligible sampling fractions. Journal of the American Statistical Association, 94, 254-265.
28. D. F. Signorini & M. C. Jones (2004). Kernel estimators for univariate binary regression. Journal of the American Statistical Association, 99(465), 119-126.
29. M. P. Wand & M. C. Jones (1995). Kernel Smoothing. Chapman & Hall, London.