References. BEAUMONT, J.F., An estimation method for nonignorable nonresponse, Survey Methodology, 26, , 2000.

Similar documents
A Comparison of Approximate Bayesian Bootstrap and Weighted Sequential Hot Deck for Multiple Imputation

RELATIVE EFFICIENCY OF ESTIMATES BASED ON PERCENTAGES OF MISSINGNESS USING THREE IMPUTATION NUMBERS IN MULTIPLE IMPUTATION ANALYSIS ABSTRACT

This module is part of the. Memobust Handbook. on Methodology of Modern Business Statistics

Multiple Imputation for Missing Data in KLoSA

Multiple Imputation of Turnover in EDINET Data: Toward the Improvement of Imputation for the Economic Census

Flexible Imputation of Missing Data

Evaluation of Alternative Imputation Methods for 2017 Economic Census Products 1 Jeremy Knutson and Jared Martin

Missing Data Treatments

Missing data in political science

Michael Bankier, Jean-Marc Fillion, Manchi Luc and Christian Nadeau Manchi Luc, 15A R.H. Coats Bldg., Statistics Canada, Ottawa K1A 0T6

Imputation Procedures for Missing Data in Clinical Research

Missing value imputation in SAS: an intro to Proc MI and MIANALYZE

Handling Missing Data. Ashley Parker EDU 7312

Variance Estimation of the Design Effect

Imputation of multivariate continuous data with non-ignorable missingness

Multiple Imputation Scheme for Overcoming the Missing Values and Variability Issues in ITS Data

Method for the imputation of the earnings variable in the Belgian LFS

Copyright is owned by the Author of the thesis. Permission is given for a copy to be downloaded by an individual for the purpose of research and

Improving Capacity for Crime Repor3ng: Data Quality and Imputa3on Methods Using State Incident- Based Repor3ng System Data

A Comparison of Imputation Methods in the 2012 Behavioral Risk Factor Surveillance Survey

1. Expressed in billions of real dollars, seasonally adjusted, annual rate.

Caffeine and Theobromine Intakes of Children: Results From CSFII , 1998

Missing Data Imputation Method Comparison in Ohio University Student Retention. Database. A thesis presented to. the faculty of

Flexible Working Arrangements, Collaboration, ICT and Innovation

Missing Data Methods (Part I): Multiple Imputation. Advanced Multivariate Statistical Methods Workshop

A study on consumer perception about soft drink products

COMPARISON OF CORE AND PEEL SAMPLING METHODS FOR DRY MATTER MEASUREMENT IN HASS AVOCADO FRUIT

DETERMINANTS OF DINER RESPONSE TO ORIENTAL CUISINE IN SPECIALITY RESTAURANTS AND SELECTED CLASSIFIED HOTELS IN NAIROBI COUNTY, KENYA

PEEL RIVER HEALTH ASSESSMENT

Zeitschrift für Soziologie, Jg., Heft 5, 2015, Online- Anhang

Sponsored by: Center For Clinical Investigation and Cleveland CTSC

Dietary Diversity in Urban and Rural China: An Endogenous Variety Approach

IMPUTING FOR MISSING SURVEY RESPONSES Graham Kalton, University of Michigan Daniel Kasprzyk, Social Security Administration i.

A Comparison of Price Imputation Methods under Large Samples and Different Levels of Censoring.

7 th Annual Conference AAWE, Stellenbosch, Jun 2013

The Development of a Weather-based Crop Disaster Program

Effects of Information and Country of Origin on Chinese Consumer Preferences for Wine: An Experimental Approach in the Field

Problem Set #3 Key. Forecasting

Notes on the Philadelphia Fed s Real-Time Data Set for Macroeconomists (RTDSM) Capacity Utilization. Last Updated: December 21, 2016

Economic Losses from Pollution Closure of Clam Harvesting Areas in Machias Bay

Chained equations and more in multiple imputation in Stata 12

Keywords: Correspondence Analysis, Bootstrap, Textual analysis, Free-text comments.

AST Live November 2016 Roasting Module. Presenter: John Thompson Coffee Nexus Ltd, Scotland

IMPUTING NUMERIC AND QUALITATIVE VARIABLES SIMULTANEOUSLY

Gasoline Empirical Analysis: Competition Bureau March 2005

Notes on the Philadelphia Fed s Real-Time Data Set for Macroeconomists (RTDSM) Indexes of Aggregate Weekly Hours. Last Updated: December 22, 2016

Activity 10. Coffee Break. Introduction. Equipment Required. Collecting the Data

Final Exam Financial Data Analysis (6 Credit points/imp Students) March 2, 2006

Missing Data: Part 2 Implementing Multiple Imputation in STATA and SPSS. Carol B. Thompson Johns Hopkins Biostatistics Center SON Brown Bag 4/24/13

Learning Connectivity Networks from High-Dimensional Point Processes

Classification Bias in Commercial Business Lists for Retail Food Outlets in the U.S

1. Continuing the development and validation of mobile sensors. 3. Identifying and establishing variable rate management field trials

Transportation demand management in a deprived territory: A case study in the North of France

Predicting Wine Quality

Imputation Variance Estimation for Statistics New Zealand s Accommodation Occupancy Survey

Senior poverty in Canada, : A decomposition analysis of income and poverty rates

Prevalence of food allergies: What is KNOWN What is UNKNOWN

Growth in early yyears: statistical and clinical insights

The Effect of Almond Flour on Texture and Palatability of Chocolate Chip Cookies. Joclyn Wallace FN 453 Dr. Daniel

Tree Rings and Water Resource Management in the Southwest

Comparative Analysis of Fresh and Dried Fish Consumption in Ondo State, Nigeria

Volume 30, Issue 1. Gender and firm-size: Evidence from Africa

A Note on a Test for the Sum of Ranksums*

Memorandum of understanding

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

Labor Supply of Married Couples in the Formal and Informal Sectors in Thailand

ASSESSING THE HEALTHFULNESS OF FOOD PURCHASES AMONG LOW-INCOME AREA SHOPPERS IN THE NORTHEAST

The aim of the thesis is to determine the economic efficiency of production factors utilization in S.C. AGROINDUSTRIALA BUCIUM S.A.

What are the Driving Forces for Arts and Culture Related Activities in Japan?

AJAE Appendix: Testing Household-Specific Explanations for the Inverse Productivity Relationship

Microanalytical Quality of Ground and Unground Marjoram, Sage and Thyme, Ground Allspice, Black Pepper and Paprika

Computerized Models for Shelf Life Prediction of Post-Harvest Coffee Sterilized Milk Drink

New from Packaged Facts!

Evaluating Population Forecast Accuracy: A Regression Approach Using County Data

Beer bitterness and testing

Developments in the legislation on food hygiene related with VTEC Kris De Smet European Commission GD SANCO, Unit G4 Food, alert system and training

Long term impacts of facilitating temporary contracts: A comparative analysis of Italy and Spain using birth cohorts

Wine-Tasting by Numbers: Using Binary Logistic Regression to Reveal the Preferences of Experts

Effect of Inocucor on strawberry plants growth and production

The substitutability among Japanese, Taiwanese and South Korean fronzen tuna

The Economics of Dollarware

Appendix A. Table A.1: Logit Estimates for Elasticities

MARKET ANALYSIS REPORT NO 1 OF 2015: TABLE GRAPES

2. The proposal has been sent to the Virtual Screening Committee (VSC) for evaluation and will be examined by the Executive Board in September 2008.

Bishop Druitt College Food Technology Year 10 Semester 2, 2018

wine 1 wine 2 wine 3 person person person person person

November K. J. Martijn Cremers Lubomir P. Litov Simone M. Sepe

Gender and Firm-size: Evidence from Africa

Peanut Stocks and Processing

Peanut Stocks and Processing

Peanut Stocks and Processing

November 9, 2016 December 9, 2016 Florida FCOJ Yield 1.48 Gallons per Box

Hybrid ARIMA-ANN Modelling for Forecasting the Price of Robusta Coffee in India

TRTP and TRTA in BDS Application per CDISC ADaM Standards Maggie Ci Jiang, Teva Pharmaceuticals, West Chester, PA

COMMUNICATION II Moisture Determination of Cocoa Beans by Microwave Oven

RESEARCH VESSEL SALMONID CPUE IN RELATION TO THE NORTHERN BOUNDARY OF THE SQUID DRIFTNET FISHERY

Table A.1: Use of funds by frequency of ROSCA meetings in 9 research sites (Note multiple answers are allowed per respondent)

The Sources of Risk Spillovers among REITs: Asset Similarities and Regional Proximity

Soft and Semi-soft Cheese made from Unpasteurized/Raw Milk in Canada Bureau of Microbial Hazards, Food Directorate, Health Canada

DATA AND ASSUMPTIONS (TAX CALCULATOR REVISION, MARCH 2017)

Transcription:

References ANDERSON, C., NORBERG, L., A method for variance estimation of non-linear function of totals in surveys-theory and implementation, Journal of Official Statistics, 395-405,1994. BEAUMONT, J.F., An estimation method for nonignorable nonresponse, Survey Methodology, 26, 131-136, 2000. BANKIER, M., LUC, M., NADEAU, C., NEWCOMBE, P., Imputing numeric and qualitative census variables simultaneously, Proceedings of the Survey Research Methods Section, 90-99,1996. BINDER, D.A. A framework for analyzing categorical survey data with nonresponse, Journal of Official Statistics, 7, 393-404, 1991. BINDER, D.A., SUN, W., Frequency valid multiple imputation for surveys with a complex design, Proceedings of the Section on Survey Research Methods, American Statistical Association, 281-286, 1996. BURNS, E. M., Multiple and replicate item imputation in a complex sample survey, Proceedings of the Sixth Annual Research Conference, U. S. Bureau of the Census 655-665, 1990. BRICK, J. M., KALTON, G., Handling missing data in survey research, Stat. Math. In Med. Res., 5, 215-238, 1996. CASELLA, G., EDWARD G.I., Explaining the Gibbs Sampler, The American Statistician, 46, 3 CARON, N., Les principales techniques de correction de la non-réponse, et les modèles associés, Document de travail, Méthodologie statistique, INSEE, 9604, 1996. CHEN, Y., Balanced repeated replication variance estimators for survey data under imputation, unpublished Ph. D. thesis, University of Ottawa, Department of Mathematics and Statistics, 1993. CHEN, J., SHAO, J., Nearest-neighbour imputation for survey data, Journal of Official Statistics, 16, 583-599, 2000. CHEN, J., SHAO, J., Jackknife variance estimation for nearest-neighbor imputation, Journal of the American Statistical Association, 96, 260-269, 2001.

COTTON, C., Generalised Edit and Imputation System functional description, Statistics Canada, 1991. DALENIUS, T., Some refections on the problem of missing data In: W. G. Madow and I. Olkin (eds.), Incomplete Data in Sample Surveys, vol. 3 New York: Academic Press, 411-413 1983. DEVILLE, J.-C., SÄRNDAL, C.-E., Estimation de la variance en présence de données imputées, proceedings of Invited Papers for the 48th Session of the International Statistical Institute, Book 2, Subject 17, 3e17, 1991. DEVILLE, J.-C., SÄRNDAL, C.-E., Variance estimation for the regression imputed Horvitz- Thompson estimator, Journal of Official Statistics, 10, 381-394, 1994. DROESBEKE, J.-J., LAVALLÉE, P., La non-réponse dans les enquêtes, Méthodologica, 4, 1996. ELTINGE, J. L., YANSANEH, I.S., Diagnostics for formation of nonresponse adjustment cells, with an application to income nonresponse in the U. S. Consumer Expenditure Survey, Survey Methodology, vol. 23, 33-40, 1997. ESTEVAO, V., HIDIROGLOU, M.A., SÄRNDAL, C.-E., Methodological principles for a generalized estimation system at Statistics Canada, Journal of Official Statistics, 11, 181-204, 1995. FELLEGI, I.P., HOLT, D., A systematic approach to automatic edit and imputation, Journal of the American Statistical Association, 71, 17-35, 1976. FAY, R. E., A design-based perspective on missing data variance, Proceedings of the 1991 Annual Research Conference, U. S. Bureau of the Census, 429-440, 1991. FAY, R. E., Alternative paradigms for the analysis of imputed survey data, Journal of the American Statistical Association,. 91, 490-498, 1996. FORD, B. M., An overview of hot-deck procedures In: W. G. Madow and I. Olkin (eds.), Incomplete Data in Sample Surveys, vol. 2 New York: Academic Press, 185-207 1983. FRIEDMAN, L.M., FURBERG, C.D., DeMETS, D.L., Fundementals of Clinical Trials, Springer, New York, 1998 GAGNON, F., LEE, H., RANCOURT, E., SÄRNDAL, C.-E., Estimating the variance of the Generalized regression estimator in the presence of imputation for the Generalized Estimation System, Proceedings of the Survey Methods Section, Statistical Society of Canada, 151-156, June 1996.

GAGNON, F., LEE, H., PROVOST, M., RANCOURT, E., SÄRNDAL, C.-E., Estimation of the variance in presence of imputation, Proceedings of Symposium 97 : New Directions in Surveys and Censuses, 273-277, Statistics Canada, Ottawa, November 1997. GELFAND, A.E., SMITH, A.F.M., Sampling based approach to calculating marginal densities, Journal of the American Statistical Association, 85, 398-409, 1990. GILKS, W.R, CLAYTON, D.G., et al. Modelling Compelexity: Applications of Gibbs Sampling in Medicine, Journal of the Royal Statistical Society, 55, 1, 39-52, 1993. GROSS, S., Median estimation in sample surveys, Proceedings of the Section on Survey Research Methods, American Statistical Association, 181-184, 1980. GROVES, R. and COOPER, M. Nonresponse in Household Surveys, John Wiley and Sons, 1998. HANSEN, M.H., HURWITZ, W.N., The problem of nonresponse in sample surveys, Journal of the American Statistical Association, 41, 517-529, 1946. HAZIZA, D., CHARBONNIER, C., CHOW, O., BEAUMONT, J.-F. Cpnstruction of imputation cells in the context of the Canadian Labour Force Survey, Proceedings of Statistics Canada s Symposium 2001: Achieving Data Quality in a Statistical Agency - A Methodological Perspective, to appear, 2001. HAZIZA, D., RAO, J.N.K., Inference for regression coefficient under imputation for missing data, Proceedings of the Survey Methods Section, Statistical Society of Canada, to appear, 2001. HAZIZA, D., RAO, J.N.K., Model-assisted approach to inference for totals under imputation for missing data, Proceedings of the Survey Methods Section, American Statistical Association, 2001. HAZIZA, D., RAO, J.N.K., Inference for population means under unweighted imputation for missing survey data, submitted, 2002. HANSEN, M.H., HURWITZ, W.N., The problem of nonresponse in sample surveys, Journal of the American Statistical Association, 41, 517-529, 1946. HEERINGA, S.G., LEPKOWSKI, J.M. Longitudinal imputation for the SIPP, Proceedings of the Section on Survey Research Methods, American Statistical Association, 206-210, 1986. HIDIROGLOU, M.A., DREW, J.D., GRAY, G.B., A framework for measuring and reducing nonresponse in surveys, Survey Methodology, 19, 81-94, 1993 HU, M., SALVUCCI, S.M., COHEN, M.P., Evaluation of some popular imputation algorithms, Proceedings of the Section on Survey Research Methods, American Statistical Association, to appear, 1998. JUDKINS, D.R., Imputing for Swiss cheese patterns of missing data, Proceedings of Statistics Canada Symposium 97, New Directions in Surveys and Censuses, 143-148, 1999.

KALTON, G., Handling wave nonresponse in panel surveys, Journal of Official Statistics, 2, 303-314, 1986. KALTON, G., KASPRZYK, D., The treatment of missing survey data, Survey Methodology, 12, 1-16, 1986. KALTON, G., KISH, L., Some efficient random imputation methods, Communications in Statistics, 13, 1919-1939, 1984. KOTT, P. S., A paradox of multiple imputation, Proceedings of the Section on Survey Research Methods, American Statistical Association, 380-383, 1995. KOVAR, J.G., CHEN, E., Jackknife variance estimation of imputed survey data, Survey Methodology, 20, 45-52, 1994. KOVAR, J.G., WHITRIDGE, P.J., Imputation of business survey data, Business Survey Methods, Cox, B.G., Binder, D.A., Chinnappa, B.N., Christianson, A., Colledge, M.J. and Kott, P.S. (ed.), 403-423, New York: John Wiley and Sons, 1995. KRENZKE, T., MOHADJER, L., MONTEQUILA, J., Generalizing the imputation error variance in the alcohol and drug services study, Proceedings of the Section on Survey Research Methods, American Statistical Association, 118-123, 1998. LAIRD, N.M. Missing data in longitudinal studies, Statistics In Medicine, 7, 305-315, 1988 LEE, H., RANCOURT, E., SÄRNDAL, C.-E., Experiment with variance estimation from survey data with imputed values, Journal of Official Statistics, 10, 231-243, 1994. LEE, H., RANCOURT, E., SÄRNDAL, C.-E., Jackknife variance estimation for data with imputed Values, Proceedings of the Survey Methods Section, 111-115, Statistical Society of Canada, July 1995. LEE, H., RANCOURT, E., SÄRNDAL, C.-E., Variance estimation in the presence of imputed data for the Generalized Estimation System, Proceedings of the Section on Survey Research Methods, American Statistical Association, 384-389, August 1995. LEE, H., RANCOURT, E., SÄRNDAL, C.-E., Variance estimation from survey data under single value imputation, In: Survey Nonresponse, Groves, R., Dillman, D., Eltinge, J. and Little, R.J.A. (eds), John Wiley, 315-328, 2002. LI, K.H., Imputation using Markov Chains. Journal of Statistical Compuatioan and Simulation, 30, 57-79, 1988 LITTLE, R.J.A., Models for nonresponse in sample surveys. Journal of the American Statistical Association, 77, 237-250, 1982.

LITTLE, R.J.A., Survey nonresponse adjustments for estimates of means. International Statistical Review, 54, 139-157, 1986. LITTLE, R.J.A., RUBIN, D.B. Statistical analysis with missing data. New York, John Wiley and Sons. 1987. MONTEQUILA, J. M., JERNIGAN, R. W., Variance estimation in the presence of imputed data, Proceedings of the Section on Survey Research Methods, American Statistical Association, 273-278,1997. OH, H.L., SCHEUREN, F.J., Weighting adjustment for unit nonresponse, in Incomplete Data in Sample Surveys, vol. 2, ed. : W.G. Madow, I. Olkin and D.B. Rubin, New York : Academic Press, 143-184, 1983. OUTRATA, E., CHINNAPPA, B.N., General survey functions design at Statistics Canada, Bulletin of the International Statistical Institute, 53 : 2, 219-238, 1989. PANEL ON INCOMPLETE DATA, Incomplete Data in Sample Surveys, 3 volumes, Academic Press, 1983. PROVOST, M., Estimation de la variance dans les sondages utilisant l imputation hot-deck. Master s thesis, Université de Montréal, 1995. PIANTADOSI, S, Clinical Trials, Wiley, New York, 1997 QIN, J., LEUNG, D., SHAO, J., Estimation with survey data under nonignorable nonresponse or informative sampling. Journal of the American Statistical Association, 97, 193-200, 2002. RANCOURT, E., Issues in the combined use of Statistics Canada s Generalized Edit and Imputation System and Generalized Estimation System, Survey and Statistical Computing : Proceedings of The Second ASC International Conference, Association for Survey Computing, 185-194, September 1996. RANCOURT, E., Estimation de variance en présence d imputation par valeur précédente, Presented at the Colloque francophone sur les sondages, Rennes, June 1997. RANCOURT, E., Estimation de variance en présence d imputation : Où en sommes-nous?, presented at the Journées de méthodologie statistique, Paris, March 1998. RANCOURT, E., Estimation with nearest neighbour imputation at Statistics Canada, Proceedings of the Section on Survey Research Methods, American Statistical Association, 131-138, 1999. RANCOURT, E., Edit and Imputation: from suspicious to scientific techniques, manuscript, 2001.

RANCOURT, E., LEE, H., SÄRNDAL, C.-E., Variance estimation under more than one imputation method, Proceedings of the International Conference on Establishment Surveys, American Statistical Association, 374-379, June 1993. RANCOURT, E., LEE, H., SÄRNDAL, C.-E., Bias corrections for survey estimates from data with ratio imputed values for confounded nonresponse, Survey Methodology, 20, 137-147, 1994. RANCOURT, E., LEE, H., SÄRNDAL, C.-E., Estimation of the variance in the presence of nearest neighbour imputation, Proceedings of the Section on Survey Research Methods, American Statistical Association, 888-893, 1994. RAO, J.N.K., Variance estimation under imputation for missing data. Technical report, Statistics Canada, Ottawa, 1990. RAO, J.N.K., Linearization variance estimators under imputation for missing data. Technical report, Statistics Canada, Ottawa, 1993. RAO, J.N.K., On variance estimation with imputed survey data. Journal of the American Statistical Association, 91, 499-506, 1996. RAO, J.N.K., SHAO, J., Jackknife variance estimation with survey data under hot-deck imputation. Biometrika, 79, 811-822, 1992. RAO, J.N.K., SHAO, J., On balanced half-sample variance estimation in stratified sampling. Journal of the American Statistical Association, 91, 343-348, 1996. RAO, J.N.K., SITTER, R.R., Variance estimation under two-phase sampling with application to imputation for missing data, Biometrika, 82, 453-460, 1995. RAO, J.N.K., WU, C.F.J., Resampling inference with complex survey data, Journal of the American Statistical Association, 83, 231-241, 1988. ROBERT, C.P., CASELLA, Monte Carlo Statistical Methods, Spinger-Verlag, New York, 1999 RUBIN, D.B., Inference and missing data, Biometrika, 63, 581-590, 1976. RUBIN, D.B., Formalizing subjective notions about the effect of nonrespondents in sample surveys. Journal of the American Statistical Association, 77, 538-543, 1977. RUBIN, D.B., Multiple imputation in sample surveys - a phenomenological Bayesian approach to nonresponse. Proceedings of the Section on Survey Research Methods, American Statistical Association, 20-34, 1978.

RUBIN, D.B., Basic ideas of multiple imputation for nonresponse, Survey Methodology, 12, 37-47, 1986. RUBIN, D.B., Multiple imputation for nonresponse in surveys, New York: John Wiley and Sons, 1987. RUBIN, D.B., Multiple imputation after 18+ years, Journal of the American Statistical Association, vol. 91, 473-489, 1996. RUBIN, D.B., SCHENKER, N., Multiple imputation for interval estimation from simple random samples with ignorable nonresponses, Journal of the American Statistical Association, vol. 81, 366-374, 1986. RUST, K.F., RAO, J.N.K., Variance estimation for complex surveys using replication techniques, Statistical Methods in Medical Research, 5, 283-310, 1996. SANTOS, R., Effects of imputation on regression-coefficients, Proceedings of the Section on Survey Research Methods, American Statistical Association, 140-145, 1981. SÄRNDAL, C.-E., Methods for estimating the precision of survey estimates when imputation has been used, Proceedings of Symposium 90: Measurement and improvement of data quality, 337-347. Statistics Canada, Ottawa, 1990. SÄRNDAL, C.-E., Methods for estimating the precision of survey estimates when imputation has been used, Survey Methodology, 18, 241-252, 1992. SÄRNDAL, C.-E., For a better understanding of imputation, Proceedings of the 6th Workshop on Household Survey Nonresponse, Helsinki, October 1995. SCHAFER, J.-L., Analysis of incomplete multivariate data. Chapman and Hall, London, 1997. SHAO, J., Replication Methods for variance estimation in complex surveys with imputed data, In: Survey Nonresponse, Groves, R., Dillman, D., Eltinge, J. and Little, R.J.A. (eds), John Wiley, 303-314, 2002. SHAO, J., CHEN, Y, CHEN, Y., Balanced repeated replication for stratified multistage survey data under imputation, Journal of the American Statistical Association, 93, 819-831, 1998. SHAO, J., SITTER, R.R., Bootstrap for imputed survey data, Journal of the American Statistical Association, 91, 1278-1288, 1996. SHAO, J., STEEL, P., Variance estimation for survey data with composite imputation and nonnegligible sampling fractions, Journal of the American Statistical Association, 94, 254-265, 1999.

SHAO, J., TU, D., The jackknife and the bootstrap, New York: Springer, 1995. SITTER, R.R., A resampling procedure for complex survey data, Journal of the American Statistical Association, 87, 755-765, 1992. SITTER, R.R., Comparing three bootstrap methods for survey data, Canadian Journal of Statistics, 20, 135-154, 1992. SITTER, R.R., RAO, J.N.K. Imputation for missing values and corresponding variance estimation, Canadian Journal of Statistics, 25, 61-73, 1997. SKINNER, C.J., RAO, J.N.K. Jackknife variance estimation for multivariate statistics under hotdeck imputation from common donors, Journal of Statistical Planning and Inference, 149-167, 2002. STATISTICS CANADA (2001). Standards and Guidelines for Reporting of Nonresponse Rates. Statistics Canada Technical Report. STEEL, P., FAY, R.E., Variance estimation for finite populations with imputed data, Proceedings of the Section on Survey Research Methods, American Statistical Association, 374-379, 1995. SWAIN, L., DOLSON, D., Current issues in household survey nonresponse at Statistics Canada, presented at the 8 th International Workshop on Household Survey Nonresponse, 1997. TANNER, M.A., WONG, W.H. The calculation of posterior distributions by data augmentation (with discussion), Journal of the American Statistical Association, 82, 528-550, 1987. TOLLEFSON, M., FULLER, W. A., Variance estimation for samples with random imputation, Proceedings of the Section on Survey Research Methods, American Statistical Association, 758-763, 1992. WANG, N., ROBINS, J.M. Large sample inference in parametric multiple imputation, Biometrika, 85, 935-948, 1998. WHITRIDGE, P., Encouraging response in agricultural surveys, Proceedings Symposium 96: Nonsampling errors, Statistics Canada, Ottawa, 95-101, 1996. WHITRIDGE, P., BUREAU, M. KOVAR, J. Mass Imputation at Statistics Canada, Proceedings of the Annual Research Conference, US Census Bureau, Washington, 666-675, 1990. WHITRIDGE, P., BUREAU, M. KOVAR, J. Use of Mass Imputation to Estimate for Subsample Variables, Proceedings of the Business and Economic Statistics Section, American Statistical Association, 132-137, 1990.

YUNG, W., RAO, J.N.K. Jackknife variance estimation under imputation for estimators using poststratification information, Journal of the American Statistical Association, 95, 903-915, 2000. ZANUTTO, E., Jackknife variance estimation under imputation for missing data in survey samples, Master of Science Thesis, Carleton University, Ottawa, 1993. WEB Donald Hedeker - longitudinal missing data: http://www.uic.edu/~hedeker