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2014 | 15 | nr 3 | 369--388
Tytuł artykułu

Estimating Population Mean with Missing Data in Unequal Probability Sampling

Autorzy
Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Nonresponse problem is a serious obstacle to the validity of estimates in a survey. The estimates become biased due to the missing values in data. The problem is how to deal with missing values, once they have been deemed impossible to recover. One way of exploring a possible lack of representativity in missing data is to estimate the response probabilities which are usually done by logistic regression model. However, the drawback of the logit model is that this requires values of the explanatory variables of the model to be known for all nonrespondents. Bethlehem (2012) showed that the response probabilities can be estimated by some weighting adjustment technique without having the individual data of the nonrespondents. Here we consider the doubtful nature of nonresponse regarding possible existence of relationship with any of the covariates. Moreover, instead of simple random sampling, we consider general unequal probability sampling scheme for selecting respondents. This paper presents the modification of Bethlehem (2012) proposal for unequal probability sampling to obtain the unbiased estimators for population total/average of a variable of interest and variance estimator and compares them with the usual estimators through numerical simulations. (original abstract)
Słowa kluczowe
Rocznik
Tom
15
Numer
Strony
369--388
Opis fizyczny
Twórcy
  • Indian Statistical Institute, India
Bibliografia
  • BETHLEHEM, J. G., (2012). Using response probabilities for assessing representativity. Statistics Netherlands, Discussion Paper.
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  • CHANG, T., KOTT, P. S., (2008). Using calibration weighting to adjust for nonresponse under a plausible model. Biometrika. 95, 557-571.
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  • CHAUDHURI, A., PAL, S., (2002). Estimating proportions from unequal probability samples using randomized responses by Warner's and other devices. Journal of the Indian Society of Agricultural Statistics. 55(2), 174-183.
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  • KOTT, P. S., (2006). Using calibration weighting to adjust for nonresponse and coverage errors. Survey Methodology. 32, 133-142.
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  • PÖLITZ, A. N., SIMMONS, W. R., (1949). An Attempt to Get 'Not-at-Homes' into the Sample Without Call- Backs. Journal of the American Statistical Association. 44, 9-31.
  • POLITZ, A., SIMMONS, W., (1950). Note on an Attempt to Get 'Not-at-Homes' into the Sample Without Call-Backs. Journal of the American Statistical Association. 45, 136-137.
  • RAJ, D., (1966). Some remarks on a simple procedure of sampling without replacement. Journal of the American Statistical Association. 61, 391-396.
  • RUBIN, D. B., (1987). Multiple Imputation for Nonresponse in Surveys. J. Wiley & Sons, New York.
  • RUBIN, D. B., (1976). Inference and missing data. Biometrika. 63, 581-592.
  • SARNDAL, C. E., (2011). Dealing with survey nonresponse in data collection, in estimation. Journal of Official Statistics. 27, 1-21.
  • SARNDAL, C. E., SWENSON, B., WRETMAN, J., (1992). Model Assisted Survey Sampling. Springer-Verlag. New York.
  • SETH, G. R., (1966). On estimators of variance of estimate of population total in varying probabilities. Journal of the Indian Society of Agricultural Statistics. 18, 52-56.
  • SINGH, S., (2010). Layman's understanding of non-response: How Michael and Amy adjust a missing phone call. LIAISON, Statistical Society of Canada. 24(3), p. 67.
  • VALLIANT, R., DORFMAN, A. H., ROYALL, R. M., (2000). Finite Population Sampling and Inference: A Prediction Approach. Wiley Series in Survey Methodology. New York.
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Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171322689

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