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Volumn 27, Issue 2, 2011, Pages 153-180

Proxy pattern-mixture analysis for survey nonresponse

Author keywords

Bayesian methods; Missing data; Nonignorable nonresponse; Nonresponse bias analysis; Survey data

Indexed keywords


EID: 79959509358     PISSN: 0282423X     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (60)

References (31)
  • 1
    • 25444450992 scopus 로고    scopus 로고
    • Weighting nonresponse adjustments based on auxiliary information
    • R. Groves, D. Dillman, J. Eltinge, and R. Little, Chapter 18. New York: Wiley
    • Bethlehem, J. (2002). Weighting Nonresponse Adjustments Based on Auxiliary Information. In Survey Nonresponse, R. Groves, D. Dillman, J. Eltinge, and R. Little (eds). Chapter 18. New York: Wiley, 275-287.
    • (2002) Survey Nonresponse , pp. 275-287
    • Bethlehem, J.1
  • 2
    • 0025071087 scopus 로고
    • Protecting against nonrandomly missing data in longitudinal studies
    • Brown, C.H. (1990). Protecting Against Nonrandomly Missing Data in Longitudinal Studies. Biometrics, 46, 143-155.
    • (1990) Biometrics , vol.46 , pp. 143-155
    • Brown, C.H.1
  • 3
    • 4644226694 scopus 로고    scopus 로고
    • Are nonrespondents to health surveys less healthy than respondents
    • Cohen, G. and Duffy, J.C. (2002). Are Nonrespondents to Health Surveys Less Healthy than Respondents. Journal of Official Statistics, 18, 13-23.
    • (2002) Journal of Official Statistics , vol.18 , pp. 13-23
    • Cohen, G.1    Duffy, J.C.2
  • 4
    • 0034346848 scopus 로고    scopus 로고
    • The effects of response rate changes on the index of consumer sentiment
    • Curtain, R., Presser, S., and Singer, E. (2000). The Effects of Response Rate Changes on the Index of Consumer Sentiment. Public Opinion Quarterly, 64, 413-428.
    • (2000) Public Opinion Quarterly , vol.64 , pp. 413-428
    • Curtain, R.1    Presser, S.2    Singer, E.3
  • 5
    • 0033636008 scopus 로고    scopus 로고
    • Reparameterizing the pattern mixture model for sensitivity analyses under informative dropout
    • Daniels, M.J. and Hogan, J.W. (2000). Reparameterizing the Pattern Mixture Model for Sensitivity Analyses under Informative Dropout. Biometrics, 56, 1241-1248.
    • (2000) Biometrics , vol.56 , pp. 1241-1248
    • Daniels, M.J.1    Hogan, J.W.2
  • 6
    • 4243828610 scopus 로고
    • Informative drop-out in longitudinal data analysis
    • Diggle, P. and Kenward, M.G. (1994). Informative Drop-Out in Longitudinal Data Analysis. Applied Statistics, 43, 49-93.
    • (1994) Applied Statistics , vol.43 , pp. 49-93
    • Diggle, P.1    Kenward, M.G.2
  • 10
    • 77956275463 scopus 로고    scopus 로고
    • Federal Committee on Statistical Methodology, Technical report, Executive Office of the President of the United States of America
    • Federal Committee on Statistical Methodology (2001). Statistical Policy Working Paper 31: Measuring and Reporting Sources of Error in Surveys. Technical report, Executive Office of the President of the United States of America.
    • (2001) Statistical Policy Working Paper 31: Measuring and Reporting Sources of Error in Surveys
  • 11
    • 33845785591 scopus 로고    scopus 로고
    • Nonresponse rates and nonresponse bias in household surveys
    • Groves, R.M. (2006b). Nonresponse Rates and Nonresponse Bias in Household Surveys. Public Opinion Quarterly, 70, 646-675.
    • (2006) Public Opinion Quarterly , vol.70 , pp. 646-675
    • Groves, R.M.1
  • 12
    • 0001766028 scopus 로고
    • The common structure of statistical models of truncation, sample selection, and limited dependent variables and a simple estimator for such models
    • Heckman, J.J. (1976). The Common Structure of Statistical Models of Truncation, Sample Selection, and Limited Dependent Variables and a Simple Estimator for Such Models. The Annals of Economic and Social Measurement, 5, 475-492.
    • (1976) The Annals of Economic and Social Measurement , vol.5 , pp. 475-492
    • Heckman, J.J.1
  • 17
    • 77956890002 scopus 로고
    • A class of pattern-mixture models for normal incomplete data
    • Little, R.J.A. (1994). A Class of Pattern-Mixture Models for Normal Incomplete Data. Biometrika, 81, 471-483.
    • (1994) Biometrika , vol.81 , pp. 471-483
    • Little, R.J.A.1
  • 18
    • 2942567603 scopus 로고    scopus 로고
    • To model or not to model? Competing modes of inference for finite population sampling
    • Little, R.J.A. (2004). To Model or Not to Model? Competing Modes of Inference for Finite Population Sampling. Journal of the American Statistical Association, 99, 546-556.
    • (2004) Journal of the American Statistical Association , vol.99 , pp. 546-556
    • Little, R.J.A.1
  • 20
    • 27744441565 scopus 로고    scopus 로고
    • Does weighting for nonresponse increase the variance of survey means?
    • Little, R. and Vartivarian, S. (2005). Does Weighting for Nonresponse Increase the Variance of Survey Means? Survey Methodology, 31, 161-168.
    • (2005) Survey Methodology , vol.31 , pp. 161-168
    • Little, R.1    Vartivarian, S.2
  • 21
    • 4544228988 scopus 로고    scopus 로고
    • Analysis of complex survey samples
    • Lumley, T. (2004). Analysis of Complex Survey Samples. Journal of Statistical Software, 9, 1-19.
    • (2004) Journal of Statistical Software , vol.9 , pp. 1-19
    • Lumley, T.1
  • 22
    • 33845327389 scopus 로고    scopus 로고
    • Office of Management and Budget, Technical report, Executive Office of the President of the United States of America
    • Office of Management and Budget (2006). Standards and Guidelines for Statistical Surveys. Technical report, Executive Office of the President of the United States of America.
    • (2006) Standards and Guidelines for Statistical Surveys
  • 23
    • 5344244656 scopus 로고    scopus 로고
    • R Development Core Team, R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0
    • R Development Core Team (2008). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.
    • (2008) R: A Language and Environment For Statistical Computing
  • 24
    • 0017133178 scopus 로고
    • Inference and Missing Data (with Discussion)
    • Rubin, D.B. (1976). Inference and Missing Data (with Discussion). Biometrika, 63, 581-592.
    • (1976) Biometrika , vol.63 , pp. 581-592
    • Rubin, D.B.1
  • 27
    • 85044812667 scopus 로고    scopus 로고
    • Assessing auxiliary vectors for control of nonresponse bias in the calibration estimator
    • Särndal, C.E. and Lundström, S. (2008). Assessing Auxiliary Vectors for Control of Nonresponse Bias in the Calibration Estimator. Journal of Official Statistics, 24, 167-191.
    • (2008) Journal of Official Statistics , vol.24 , pp. 167-191
    • Särndal, C.E.1    Lundström, S.2
  • 28
    • 77949527325 scopus 로고    scopus 로고
    • Indicators for the representativeness or survey response
    • Schouten, B., Cobben, F., and Bethlehem, J. (2009). Indicators for the Representativeness or Survey Response. Survey Methodology, 35, 101-113.
    • (2009) Survey Methodology , vol.35 , pp. 101-113
    • Schouten, B.1    Cobben, F.2    Bethlehem, J.3
  • 29
    • 0003613384 scopus 로고
    • U.S. Department of Health and Human Services, Technical Report, National Center for Health Statistics, Centers for Disease Control and Prevention, 1988-94
    • U.S. Department of Health and Human Services (1994). Plan and Operation of the Third National Health and Nutrition Examination Survey, 1988-94. Technical report, National Center for Health Statistics, Centers for Disease Control and Prevention.
    • (1994) Plan and Operation of the Third National Health and Nutrition Examination Survey
  • 30
    • 77955804377 scopus 로고    scopus 로고
    • U.S. Department of Health and Human Services, CD-ROM, Series 11, No. 7A. Technical report, National Center for Health Statistics, Centers for Disease Control and Prevention
    • U.S. Department of Health and Human Services (2001). Third National Health and Nutrition Examination Survey (NHANES III, 1988-1994): Multiply Imputed Data Set. CD-ROM, Series 11, No. 7A. Technical report, National Center for Health Statistics, Centers for Disease Control and Prevention.
    • (2001) Third National Health and Nutrition Examination Survey (NHANES III, 1988-1994): Multiply Imputed Data Set
  • 31
    • 77952995122 scopus 로고    scopus 로고
    • The fraction of missing information as a tool for monitoring the quality of survey data
    • Wagner, J. (2010). The Fraction of Missing Information as a Tool for Monitoring the Quality of Survey Data. Public Opinion Quarterly, 74, 223-243.
    • (2010) Public Opinion Quarterly , vol.74 , pp. 223-243
    • Wagner, J.1


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