Literature  
Find here over 440 references in multiple imputation.
 
The list below is a complete list of papers and reports on both theoretical developments and applications of multiple imputation. If you know other relevant work, please let me know.

PUBLICATIONS RELATED TO MULTIPLE IMPUTATION

Maintained by Stef van Buuren, TNO Prevention and Health, Leiden (Jan. 3, 2003)

 

Aerts M.; Claeskens G.; Hens N.; Molenberghs G (2002). Local multiple imputation. Biometrika, 89, 2, 375-388.

Alexander, C.H. Discussion of "Schafer, J.L., Ezzatti-Rice, T.M., Johnson, W. Khare, M., Little, R.J.A. and Rubin, D.B. The NHANES III multiple imputation project.". Proceedings of the Survey Research Methods Section of the American Statistical Association, 38-39. pdf

Ali MW, Siddiqui O (2000). Multiple imputation compared with some informative dropout procedures in the estimation and comparison of rates of change in longitudinal clinical trials with dropouts. J Biopharm Stat:165-81. Abstract.

Allison, P.D. (2000). Multiple imputation for missing data: A cautionary tale. Sociological Methods and Research, 28, 301-309.

Allison, P.D. (2001). Missing Data. Thousand Oaks: Sage.

Altenburg HP, Agudo A, Berrino F, Boshuizen HC, Bueno-de-Mesquita HB, Janzon L, Le Marchand L, Linseisen J, Lukanova A, Rasmuson T, Vineis P, Riboli E, and Miller A (2002). Using multiple imputation methods to estimate relative risks in small EPIC lung cancer subsets. IARC Sci Publ. 2002;156:53-4.

Alzola, C.F. and Harrell, F.E. (1999). An introduction to S-Plus and the Hmisc and Design Libraries. Online publication, http://hesweb1.med.virginia.edu/biostat/s/index.html.

Atkinson A.C.; Cheng T.-C (2000). On robust linear regression with incomplete data. Computational Statistics and Data Analysis, 33, 361-380.

Atrostic, B. K. (1994). A multiple imputation approach to microsimulation. Proceedings of the Survey Research Methods Section of the American Statistical Association, 529-534. pdf.

Ball RD (2001). Bayesian methods for quantitative trait loci mapping based on model selection: approximate analysis using the Bayesian information criterion. Genetics;159(3):1351-64.

Bárcena, M.J. and Tusell, F. (2000). Tree-based algorithms for missing data imputation. In J.G. Bethlehem & P.G.M. van der Heijden (Eds.), COMPSTAT 2000: Proceedings in Computational Statistics (pp. 193-198). Heidelberg: Physica-Verlag.

Barnard, J. (1995). Cross-Match Procedures for Multiple-Imputation Inference: Bayesian Theory and Frequentist Evaluation. Ph.D. Thesis, Department of Statistics, University of Chicago.

Barnard, J. (2000). MiPy: A system for generating multiple imputations. In J.G. Bethlehem & P.G.M. van der Heijden (Eds.), COMPSTAT 2000: Proceedings in Computational Statistics (pp. 199-204). Heidelberg: Physica-Verlag.

Barnard, J. and Meng, X.L. (1993). Exploring Cross-Match Estimators With Multiply-Imputed Data Sets. Proceedings of the Survey Research Methods Section, American Statistical Association 1994, 894-899. pdf.

Barnard, J. and Meng, X.L. (1999). Applications of multiple imputation in medical studies: from AIDS to NHANES. Statistical Methods in Medical Research, 8, 17-36.

Barnard, J., Rubin, D.B. and Schenker, N. (1998). Multiple imputation methods. In: P. Armitage and T. Colton (Eds.), Encyclopedia of Biostatistics (pp. 2772-2780). New York: Wiley.

Barnard, J. and Rubin, D.B. (1999). Small sample degrees of freedom with multiple imputation. Biometrika, 86, 948-955.

Barnard J, Rubin DB, Schenker N (2001). Multiple imputation. In Smelser NJ and Baltes PB (eds), International encyclopedia of the social & behavioral sciences. Netherlands: Elsevier Science, 2001.

Bebchuk, D. and Betensky, R.A. (2000). Multiple imputation for simple estimation of the hazard function based on interval censored data. Statistics in Medicine, 19, 405-419.

Bechger TM, Boomsma DI, Koning H (2002). A limited dependent variable model for heritability estimation with non-random ascertained samples. Behav Genet;32(2):145-51.

Belin, T.R. and Rubin, D.B. (1990). Calibration of Errors in Computer Matching for Census Undercount (with discussion). Proceedings of the Government Statistics Section of the American Statistical Association, 124-131.

Belin, T.R.; Rubin, D.B. (1995). A Method for Calibrating False-Match Rates in Record Linkage. Journal of the American Statistical Association, 90, 430, 694-707.

Belin, T.R. and Rubin, D.B. (1995). The analysis of repeated-measures data on schizophrenic reaction times using mixture models. Statistics in Medicine, 14, 747-768.

Belin, T.R., Diffendal, G.J., Mack, S., Rubin, D.B., Schafer, J.L. and Zaslavsky, A.M. (1993). Hierarchical Logistic Regression Models for Imputation of Unresolved Enumeration Status in Undercount Estimation. (With discussion). Journal of the American Statistical Association, 88, 1149-1166.

Belin, T.R., Hu, M-Y, Young, A.S., Grusky, O. (1999). Performance of a general location model with an ignorable missing-data assumption in a multivariate mental health study. Statistics in Medicine, 18, 3123-3235.

Belin, T.R., Datt, M., Desmond, K. and Ganz, P.A. (1999). Comparing Imputation of Entire Subscales Versus Individual Items in a Study of Quality of Life Following Breast Cancer. Proceedings of the Survey Research Methods Section, American Statistical Association 1999, 813-818. pdf.

Belin T.R.; Hu M-Y.; Young A.S.; Grusky O (2000). Using Multiple Imputation to Incorporate Cases with Missing Items in a Mental Health Services Study. Health Services and Outcomes Research Methodology, 1, 7-22.

Bennett DA (2001). How can I deal with missing data in my study? Aust N Z J Public Health;25(5):464-9.

Bernaards, C.A. (1999). SOLAS for missing data analysis. Software review. Structural Equation Modeling, 6, 301-304.

Betensky, R.A. (1998). Multiple imputation for early stopping of a complex clinical trial. Biometrics, 54, 229-242.

Betensky, R.A., Tierney, C. (1997). An examination of methods for sample size recalculation during an experiment. Statistics in Medicine, 16, 2587-2598.

Binder, D.A. (1996). On Variance Estimation With Imputed Survey Data: Comment. Journal of the American Statistical Association, 91, 434, 510-512.

Binder, D.A. and Sun, W. (1996). Frequency valid multiple imputation for surveys with a complex design. Proceedings of the Survey Research Methods Section of the American Statistical Association, 281-286. pdf.

Bloxom, B., Pashley, P.J., Nicewander, W.A., and Yan, D. (1995). Linking to a Large-Scale Assessment: An Empirical Evaluation. Journal of Educational and Behavioral Statistics, 20, 1, 1-26.

Boshuizen, H.C., Izaks, G.J., van Buuren, S. and Ligthart, G.J. (1995). Bloeddruk en Sterfte Bij Hoogbejaarden. TNO-rapport 95.014, ISBN 90-6743-377-2.

Boshuizen HC, Izaks GJ, van Buuren S, Ligthart GJ. (1998). Blood pressure and mortality in community residents aged 85 and older. British Medical Journal, 316, 1780-1784.

Brancato, G., Pezzotti, P., Rapiti E., Perucci, C.A., Abeni, D., Babbalaccio, A., Rezza, G. and The Multicenter Prospective HIV Study (1997). Multiple imputation method for estimating incidence of HIV infection. International Journal of Epidemiology, 26, 1107-1114.

Brand, J.P.L. (1999). Development, Implementation and Evaluation of Multiple Imputation Strategies for the Statistical Analysis of Incomplete Data Sets. Ph.D. Thesis, Erasmus University Rotterdam. ISBN 90-74479-08-1.

Brand, J.P.L., van Buuren, S., van Mulligen, E.M., Timmers, T. and Gelsema, E. (1994). Multiple Imputation as a Missing Data Machine. In Ozbolt, J.G. (Ed.), Proceedings of the Eighteenth Annual Symposium on Computer Applications in Medical Care (SCAMC), 303-307. Hanley and Belfus, Inc., Philadelphia, PA.

Bray, I. Wright, D. E. (1998). Application of Markov chain Monte Carlo methods to modelling birth prevalence of Down syndrome. Journal of the Royal Statistical Society (Series C): Applied Statistics, 47, 589-602.

Brick, M. Review of "Multiple imputation for nonresponse in surveys" (Auth D.B. Rubin). Metrika, 36, 249-250.

Brown, J. G. (2002). Using a multiple imputation technique to merge data sets. Applied Economics Letters, 9, 311-314.

Brownstone, D. (1991). Multiple Imputations for Linear Regression Models. University of California, Irvine, Institute for Mathematical Behavioral Sciences Working Paper MBS 91-37.

Brownstone, D. and Valletta, R. (1996). Modeling Earnings Measurement Error: A Multiple Imputations Approach. In press, The Review of Economics and Statistics.

Burns, E.M. (1989). Multiple imputation in a complex sample survey. Proceedings of the Survey Research Methods Section of the American Statistical Association, 233-238. pdf.

Burns, E.M. (1990). Multiple and Replicate Item Imputation in a Complex Sample Survey. Proceedings of the Bureau of the Census Annual Research Conference, 655-665.

Burns, E.M. (1991). Multiple Imputation in the 1989 Commercial Buildings Energy Consumption Survey: Building Characteristics. CBECS Technical Note 86, Department of Energy, Washington DC.

Burns, E.M. (1993). Assessment of Energy Use in Multibuilding Facilities. Report DOE/EIA-0555(93)/1, U.S. Department of Energy, Washington, DC.

Carabin H, Gyorkos TW, Joseph L, Payment P, Soto JC (2001). Comparison of methods to analyse imprecise faecal coliform count data from environmental samples. Epidemiol Infect;126(2):181-90. Abstract.

Cavanaugh, J.E., Oleson, J.J. (2001). A Diagnostic for Assessing the Influence of Cases on the Prediction of Missing Data. Journal of the Royal Statistical Society: Series D (The Statistician), 50,

Chand, N. and Alexander, C.H. (1994). Imputing Income For An N-Person Consumer Unit. Bureau of the Census Paper Presented at the American Statistical Association Annual Meeting, Toronto, Canada.

Chao, M.T. (1994). A Short Review of Recent Survey Methods for Nonresponse. Journal of the Chinese Statistical Association, 32, 2, 169-177.

Chavance M, Manfredi R (2000). [Modeling incomplete observations]. Rev Epidemiol Sante Publique:389-400. French. Abstract.

Chen J and Shao J. (2001). Jackknife Variance Estimation for Nearest-Neighbor Imputation. Journal of the American Statistical Association, 96, 453, 260-269.

Chen, R. and Liu, J.S. (1996). Predictive Updating Methods with Application to Bayesian Classification. Journal of the Royal Statistical Society B, 58, 2.

Cheung YB (2002). Early origins and adult correlates of psychosomatic distress. Soc Sci Med;55(6):937-48.

Clarke, P.; Sacker, A (2002). Applying a multiple imputation method for mixed variable types to british household panel study data. International Journal of Circumpolar Health, 61, SUPP/1, 23.

Clark RE, Xie H, Adachi-Mejia AM, Sengupta A (2001). Substitution between Formal and Informal Care for Persons with Severe Mental Illness and Substance Use Disorders. J Ment Health Policy Econ;4(3):123-132.

Clark TG, Stewart ME, Altman DG, Gabra H, Smyth JF (2001). A prognostic model for ovarian cancer. Br J Cancer;85(7):944-52.

Clayton, D, Dunn, G, Pickles, A. and Spiegelhalter, D. (1998). Analysis of longitudinal binary data from multiphase sampling (with discussion). Journal of the Royal Statistical Society B, 60, 71-87.

Clayton D.; Rasbash J (1999). Estimation in large cross random-effect models by data augmentation. Journal of the Royal Statistical Society: Series A (Statistics in Society), 162, 425-436.

Clogg, C.C., Rubin, D.B., Schenker, N., Schultz, B. and Weidman, L. (1991). Multiple Imputation of Industry and Occupation Codes in Census Public-Use Samples Using Bayesian Logistic Regression. Journal of the American Statistical Association, 86, 413, 68-78.

Cohen, M.P. (1996) A new approach to imputation. Proceedings of the Survey Research Methods Section of the American Statistical Association, 293-298. pdf.

Cohen, M.P. (1997). The Bayesian Bootstrap and Multiple Imputation for Unequal Probability Sample Designs. Proceedings of the Survey Research Methods Section, American Statistical Association 1997, 635-638. pdf.

Collins LM, Schafer JL, Kam CM (2001). A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychological Methods.;6(4):330-51.

Cook, N.R. (1997) An imputation method for non-ignorable missing data in studies of blood pressure. Statistics in Medicine, 16, 2713-2728.

Cowles, M.K.; Carlin, B.P.; Connett, J.E (1996). Bayesian Tobit Modeling of Longitudinal Ordinal Clinical Trial Compliance Data With Nonignorable Missingness. Journal of the American Statistical Association, 91, 433, 86-98.

Cox Jr. L.A. (1999). Adaptive Spatial Sampling of Contaminated Soil. Risk Analysis, 19, 1059-1069.

Crawford, S.L., Tennstedt S.L. and Mckinlay, J.B. (1995). A comparison of analytic methods for non-random missingness of outcome data. Journal of Clinical Epidemiology, 48, 209-219.

Curran D, Molenberghs G, Fayers PM, Machin D (1998). Incomplete quality of life data in randomized trials: missing forms. Statistics in Medicine;17(5-7):697-709.

Davey, A.; Shanahan, M. J.; Schafer, J. L (2001). Correcting for Selective Nonresponse in the National Longitudinal Survey of Youth Using Multiple Imputation. Journal of Human Resources, 36, 500-519.

Decanio S. J.; Watkins W. E (1998). Investment in energy efficiency: do the characteristics of firms matter? The Review of Economics and Statistics, 80, 95-107.

de Sturler E.; Schenker N.; Taylor J.M.G. (1996). Partially parametric techniques for multiple imputation. Computational Statistics and Data Analysis, 22, 425-446.

Donze, L. (2001). L'imputation des donnees manquantes, la technique de l'imputation multiple, les consequences sur l'analyse des donnees: l'enquete 1999 KOF/ETHZ sur l'innovation. Revue Suisse d Economie Politique et de Statistique, 137, 301-318.

Dorey, F.J., Little, R.J.A. and Schenker, N. (1993). Multiple Imputation for Threshold-Crossing Data With Interval-Censoring. Statistics in Medicine, 12, 1589-1603.

Dorinski, S.M., Petroni, R.J., Ikeda, M. and Singh, R.P. (1996). Comparison and evaluation of alternative icm imputation methods. Proceedings of the Survey Research Methods Section of the American Statistical Association, 299-304. pdf.

Dorinski, S.M. and Griffin, R. (1997). Accounting for variance due to imputation in the integrated coverage measurement survey. In Proceedings of the Survey Research Methods Section of the American Statistical Association, Alexandria, VA, pp. 748-753.

Crawford, S.L., Tennstedt, S.L., McKinlay, J.B. (1995) A comparison of analytic methods for non-random missingness of outcome data. Journal of Clinical Epidemiology, 48, 209-219.

Duncan, T.E., Duncan, C.D. and Li, F. (1998). A comparison of model- and multiple imputation based approaches to longitudinal analyses with partial missingness. Structural Equations Modelling, 5, 1-21.

Dunson, D.B. (1998). Dose-dependent number of implants and implications in developmental toxicity. Biometrics, 54, 558-569.

Dunson DB (2000). Assessing overall risk in reproductive experiments. Risk Anal;20(4):429-37.

Dybowski R, Weller PR (2001). Prediction regions for the visualization of incomplete datasets. Computational Statistics, 16, 1, 25-41.

Efron, B. (1994). Missing Data, Imputation, and the Bootstrap. Journal of the American Statistical Association, 89, 463-478, with Discussion by D.B. Rubin ( 475-8) and Rejoinder ( 478-9).

Efron, B. and Tibsharani, R. (1993). An Introduction to the Bootstrap. London: Chapman and Hall.

Elliott M. R; Raghunathan T. E; Shope J. T (2002). The Effect of Duration and Delay of Licensure on Risk of Crash: A Bayesian Analysis of Repeated Time-To-Event Measures. Journal of the American Statistical Association, 97, 458, 420-431.

Eltinge, J.L. (1996). On Variance Estimation With Imputed Survey Data: Comment. Journal of the American Statistical Association, 91, 434, 513-515.

Eltinge, J.L. (1996), Discussion of imputation papers. Proceedings of the Survey Research Methods Section of the American Statistical Association, 311-313. pdf

Eltinge, J.L., Yansaneh, I.S. and Paulin, G.D. (1994). Assessment of Reported Differences Between Expenditures and Low Incomes in the U.S. Consumer Expenditure Survey. Presented at the American Statistical Association Annual Meeting, Toronto, Canada.

Enders C.K (2001). The Performance of the Full Information Maximum Likelihood Estimator in Multiple Regression Models With Missing Data. Educational and Psychological Measurement, 61, 5, 713-740.

Ezzati-Rice, T.M., Fahimi, M., Judkins, D.M, Khare M. (1993). Serial imputation of NHANES III with mixed regression and hot-deck techniques. Proceedings of the Survey Research Methods Section, American Statistical Association 1993, 292-296. pdf.

Ezzati-Rice, T.M., Khare, M., Rubin, D.B., Little, R.J.A., Schafer, J.L. (1993). A Comparison of Imputation Techniques in the Third National Health and Nutrition Examination Survey. Proceedings of the Survey Research Methods Section of the American Statistical Association 1993, 303-308. pdf.

Ezzati-Rice, T.M., Johnson, W., Khare, M., Little, R.J.A., Rubin, D.B. and Schafer, J.L. (1995). A simulation study to evaluate the performance of model-based multiple imputations in NCHS Health Examination Surveys. Proceedings of the Bureau of the Census Eleventh Annual Research Conference, 257-266.

Fairley W. B; Izenman A. J; Crunk S. M (2001). Combining Incomplete Information From Independent Assessment Surveys for Estimating Masonry Deterioration. Journal of the American Statistical Association, 96, 454, 488-499.

Fairclough, D.L. (2002). Design and Analysis of Quality of Life Studies in Clinical Trials. London: Chapman & Hall/CRC Press.

Faris PD, Ghali WA, Brant R, Norris CM, Galbraith PD, Knudtson ML (2002). Multiple imputation versus data enhancement for dealing with missing data in observational health care outcome analyses. J Clin Epidemiol;55(2):184-91.

Faucett CL, Schenker N, Taylor JM (2002). Survival analysis using auxiliary variables via multiple imputation, with application to AIDS clinical trial data. Biometrics, 58(1):37-47.

Fay, R.E. (1989). Theory and application of replicate weighting for variance calculations. Proceedings of the Survey Research Methods Section, American Statistical Association 1989, 212-217. pdf.

Fay, R.E. (1991). A Design-Based Perspective on Missing Data Variance. Proceedings of the 1991 Annual Research Conference, Washington, DC: U.S. Bureau of the Census, 429-440.

Fay, R.E. (1992). When are Inferences from Multiple Imputation Valid? In Proceedings of the Survey Research Methods Section of the American Statistical Association, Alexandria, VA, 227-232. pdf.

Fay, R.E. (1993). Valid Inferences from Imputed Survey Data. In Proceedings of the Survey Research Methods Section of the American Statistical Association, Alexandria, VA, 41-48. pdf.

Fay, R.E. (1994). Analyzing Imputed Survey Data Sets With Model Assisted Estimators. Proceedings of the Survey Research Methods Section, American Statistical Association 1994, 900-905. pdf.

Fay, R.E. (1996). Alternative paradigms for the analysis of imputed survey data. Journal of the American Statistical Association, 91, 490-498.

Fetter M. (2001). Mass Imputation of Agricultural Economic Data Missing by Design: A Simulation Study of Two Regression Based Techniques. Federal Committee on Statistical Methodology Research Conference, 2001. pdf.

Fitzgerald AP, DeGruttola VG, Vaida F (2002). Modelling HIV viral rebound using non-linear mixed effects models. Statistics in Medicine; 21(14):2093-108.

Fogarty D.J.; Blake J. (2002). Utilising Recent Advancements in Techniques for the Analysis of Incomplete Multivariate Data to Improve the Data Quality Management of Current Academic Research. Quality and Quantity, 36, 3, 277-289.

Folsom, R.E. (1993). Comment on "A potential application of single and multiple imputation techniques in a national health survey". Proceedings of the Survey Research Methods Section of the American Statistical Association, 309-311. pdf.

Frangakis, C. and Rubin, D.B. (1999). Addressing complications of intention-to-treat analysis in the combined presence of all-or-none treatment noncompliance and subsequent missing outcomes. Biometrika, 86, 366-379.

Freedman, V.A. (1990). Using SAS to Perform Multiple Imputation. Discussion Paper Series (UI-PSC-6), The Urban Institute, Washington DC.

Freedman, V.A. and Wolf, D.A. (1991). Imputation of Mother's Marital Status in National Survey of Families and Households. Discussion Paper Series (UI-PSC-8), The Urban Institute, Washington, DC.

Freedman, V.A. and Wolf, D.A. (1995) A case study on the use of multiple imputation. Demography, 32, 459-70

Gao, S. (1999). Review of "Analysis of incomplete multivariate data" (Auth: J.L. Schafer). Statistical Method in Medical Research, 8, 88-89.

Garfield R, Leu CS (2000). A multivariate method for estimating mortality rates among children under 5 years from health and social indicators in Iraq. Int J Epidemiol;29(3):510-5. Abstract.

Gauderman WJ, Thomas DC (2001). The role of interacting determinants in the localization of genes. Adv Genet;42:393-412. Abstract.

Gelfand, A.E. and Smith, A.F.M. (1990). Sampling-based Approaches to Calculating Marginal Densities. Journal of the American Statistical Association, 85, 398-409.

Gelfand, A.E. and Smith, A.F.M. (1992). Bayesian Statistics Without Tears: A Sampling-Resampling Perspective. American Statistician, 46, 84-88.

Gelman, A. and Rubin, D.B. (1992). Inference from Iterative Simulation Using Multiple Sequences (with discussion). Statistical Science, 7, 4, 457-472.

Gelman, A., Carlin, J., Stern, H. and Rubin, D.B. (1995). Bayesian Data Analysis, New York: Chapman and Hall.

Gelman, A., King, G. and Liu, C. (1998). Not asked and not answered: Multiple imputation for multiple surveys (with discussion). Journal of the American Statistical Association, 93, 846-874.

Gelman, A. and Price, P.N. (1999). All maps of parameter estimates are misleading. Statistics in Medicine, 18, 3221-3234.

Gelman, A., Raghunathan, T.E. (2001). Discussion of Arnold et al. "Conditionally specified distributions". Statistical Science, 16, 249-274.

Geskus RB (2001). Methods for estimating the AIDS incubation time distribution when date of seroconversion is censored. Statistics in Medicine;20(5):795-812. Abstract.

Gladitz, J. (1989). Review of "Multiple imputation for nonresponse in surveys" (Auth D.B. Rubin) (in German). Biometric Journal, 31, 131-132.

Glynn, R.J., Laird, N.M. and Rubin, D.B. (1986). Selection modeling versus mixture modeling with nonignorable nonresponse. With discussion. In W. Wainer (Ed.), Drawing Inferences from Self-Selected Samples, 115-151. New York: Springer-Verlag.

Glynn, R.J., Laird, N.M. and Rubin, D.B. (1993). The Performance of Mixture Models for Nonignorable Nonresponse with Follow Ups. Journal of the American Statistical Association, 88, 984-993.

Gmel G (2001). Imputation of missing values in the case of a multiple item instrument measuring alcohol consumption. Statistics in Medicine;20(15):2369-2381. Abstract.

Goetghebeur E, Ryan L (2000). Semiparametric regression analysis of interval-censored data. Biometrics, 1139-44. Abstract.

Gold, M.S., Bentler, P.M. (2000). Treatments of missing data: A Monte Carlo comparison of RBHDI, Iterative Stochastic Regression Imputation, and Expectation-Maximization. Structural Equation Modeling, 7, 319-355.

Graham, J. W. & Hofer, S. M. (1993). EMCOV.EXE User's Guide [Computer program and manual]. Alhambra, CA: USC, Department of Prevention Research.

Graham, J.W., Hofer S.M., Piccinin A.M. (1994) Analysis with missing data in drug prevention research. In L.M. Collins & L.A. Seitz (Eds.), Advances in Data Analysis for Prevention Intervention Research, pp. 13-63. NIDA Research Monographs 142, Washington, D.C.: National Instititute of Drug Abuse.

Graham, J.W., Hofer, S.M. and MacKinnon, D.P. (1996). Maximizing the usefullness of data obtained with planned missing value patterns: An application of maximum likelihood procedures. Multivariate Behavioral Research, 31, 197-218.

Graham, J.W. and Schafer, J.L. (1997) On the performance of multiple imputation for multivariate data with small sample size. Submitted.

Graham, J.W., Hofer, S.M., Donaldson, S.I., Mackinnon, D.P. and Schafer, J.L. (1997). Analysis with missing data in prevention research. In K. Bryant, M. Windle and S. West (Eds.), The science of prevention: Methodological advances from alcohol and substance abuse research, 325-366. Washington, D.C.: American Psychological Association.

Greenlees, J.S., Reece, W.S. and Zieschang, K.D. (1982). Imputation of missing values when the probability of response depends on the variable being imputed. Journal of the American Statistical Association, 77, 251-261.

Greenland, S. and Finkle, W.D. (1995). A critical look at methods for handling missing covariates in epidemiologic regression analyses. American Journal of Epidemiology, 124, 1255-1264.

Grothaus, L.; Curry, S.; Ludman, E.; Thompson, E (2000). Effects of Differing Modeling Strategies when Using Multiple Imputation in Randomized Trials where Rates of Non-Response Differ Between Treatment Groups. Proceedings- American Statistical Association Biometrics Section, 2000, 90-94.

Hansen, M.H. (1987). A Conversation with Morris Hansen. (I. Olkin, interviewer). Statistical Science, 2, 162-179.

Harrell F.E., Jr. (2001). Regression modeling strategies. With applications to linear models, logistic regression and survival analysis. New York: Springer-Verlag.

Hediger, M.L., Overpeck, M.D., McGlynn, A., Kuczmarski, R.J., Maurer, K.R., Davis, W.W. (1999). Growth and fatness at three to six years of age of children born small- or large-for-gestational age. Pediatrics, 104, e33.

Heeringa, S.G. (1993). Imputation of Item Missing Data in the Health and Retirement Survey. Proceedings of the Survey Research Methods Section of the American Statistical Association, Alexandria, VA, 107-116. pdf.

Heeringa S.G. (1995). Application of Generalized Iterative Bayesian Simulation Methods to Estimation and Inference for Coarsened Household Income and Asset Data. Proceedings of the Survey Research Methods Section of the American Statistical Association, Alexandria, VA, 42-51. pdf.

Heeringa, S.G., Little, R.J.A., Raghunathan T.E. (1997). Imputation of multivariate data on household net worth. Proceedings of the Survey Research Methods Section, American Statistical Association 1997, 135-140. pdf.

S. Heeringa, et al. (2001). Multivariate Imputation of Coarsened Survey Data on Household Wealth. In R.M. Groves, D.A. Dillman, J.L. Eltinge, R.J.A. Little (Eds.), Survey Nonresponse. New York, Wiley.

Heitjan, D.F. (1990). Coping with Age Heaping and Digit Preference: A Multiple Imputation Approach. Unpublished Paper, Center for Biostatistics and Epidemiology, Pennsylvania State University College of Medicine, Hershey, PA.

Heitjan, D.F. (1997). Annotation: what can be done about missing data? Approaches to imputation. American Journal of Public Health, 87, 548-550.

Heitjan, D.F. and Landis, J.R. (1994). Assessing Secular Trends in Blood Pressure: A Multiple-Imputation Approach. Journal of the American Statistical Association, 89, 750-759.

Heitjan, D.F. and Little, R.J.A. (1988). Multiple Imputation for the Fatal Accident Reporting System. Proceedings of the Survey Research Methods Section of the American Statistical Association, Alexandria, VA, 93-102. pdf.

Heitjan, D.F. and Little, R.J.A. (1991). Multiple Imputation for the Fatal Accident Reporting System. Applied Statistics, 40, 13-29.

Heitjan, D.F. and Rubin, D.B. (1986). Inference from Coarse Data using Multiple Imputation. Computer Science and Statistics, Proceedings of the 18th Symposium on the Interface, 138-143.

Heitjan, D.F. and Rubin, D.B. (1990). Inference from Coarse Data via Multiple Imputation with Application to Age Heaping. Journal of the American Statistical Association, 85, 304-314.

Hendriks, J.C., Medley, G.F., van Griensven, G.J., Coutinho, R.A., Heisterkamp, S.H., van Druten, H.A. (1993). The treatment-free incubation period of AIDS in a cohort of homosexual men. Statistics in Medicine, 7, 231-239.

Heo M, Leibel RL, Boyer BB, Chung WK, Koulu M, Karvonen MK, Pesonen U, Rissanen A, Laakso M, Uusitupa MI, Chagnon Y, Bouchard C, Donohoue PA, Burns TL, Shuldiner AR, Silver K, Andersen RE, Pedersen O, Echwald S, Sorensen TI, Behn P, Permutt MA, Jacobs KB, Elston RC, Hoffman DJ, Allison DB (2001). Pooling analysis of genetic data: the association of leptin receptor (LEPR) polymorphisms with variables related to human adiposity.Genetics;159(3):1163-78

Herrchen, B., Gould, J.B., Nesbitt, T.S. (1997). Vital statistics linked birth/infant death and hospital discharge record linkage for epidemiological studies. Computational Biomedical Research, 30, 290-305.

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