Peer-reviewed publications
*Indicates a PhD or a M.Sc. student
[68] CHEN, S., HAZIZA, D. & MICHAL*, V. (2023). Efficient multiply robust imputation in the presence of influential units in survey. To appear in the Canadian Journal of Statistics.
[67] CHEN, S., & HAZIZA, D. (2023). Doubly and multiply robust imputation procedures in surveys. The Survey Statistician 88, 75-85. Invited submission.
[66] ARDILLY, P., HAZIZA, D., LAVALLÉE, P & TILLÉ, Y. (2023). On the contributions of Jean-Claude Deville to the theory of survey sampling and official statistics. To appear in Survey Methodology. Invited submission.
[65] CHEN, S. & HAZIZA, D. (2023). A unified framework of multiply robust estimation approaches for handling incomplete data. Computational Statistics and Data Analysis 179, pp. 1-17
[64] DAGDOUG*, M., GOGA, C. & HAZIZA, D. (2023). Model-assisted estimation through random forests in finite population sampling. Journal of the American Statistical Association 542, 1234-1251.
[63] DAGDOUG*, M., GOGA, C. & HAZIZA, D. (2023). Model-assisted estimation in high-dimensional settings for survey data. Journal of Applied Statistics 50, 761-785.
[62] CHEN, S. & HAZIZA, D. (2023). General purpose multiply robust data integration procedures for handling non-probability samples. Scandinavian Journal of Statistics 50, 697-724.
[61] DAGDOUG*, M., GOGA, C. & HAZIZA, D. (2023). Imputation procedures in surveys using nonparametric and machine learning methods: an empirical comparison. Journal of Survey Statistics and Methodology 11, 141-188.
[60] GOCHANOUR, B., CHEN, S., BEEBE, L. & HAZIZA, D. (2023). A nonparametric multiply robust multiple imputation method for causal inference. Metrika 86, 517-542.
[59] CHEN, S., HAZIZA, D and MASHREGHI, Z. (2022). A comparison of existing bootstrap algorithms for multi-stage sampling.
STATS 5, 521-537. nvited submission for a special issue on Bootstrap methods
[58] NEUSY, E., BEAUMONT, J.-F., YUNG, W., HIDIROGLOU, M., & HAZIZA, D. (2022). Nonresponse Follow-up for Business surveys. Survey Methodology 48, 95-117.
[57] BEAUMONT, J.-F. & HAZIZA, D. (2022). Statistical inference from finite population samples: a critical review of frequentist and
Bayesian approaches. Canadian Journal of Statistics 50, 1186-1212. Invited submission.
[56] ZHAO, P., HAZIZA, D. & WU, C. (2022). Empirical Likelihood Inference for Complex Surveys and the Design-based Oracle Variable Selection Theory. Statistica Sinica, 32, 435-457.
[55] HAZIZA, D., CHEN, S. & GAO*, Y. (2022). Targeting key survey variables at the nonresponse treatment stage. Journal of Survey Statistics and Methodology, 10, 25-49.
[54] CHEN, S., HAZIZA, D. & STUBBLEFIELD, A. (2021). A note on multiply robust predictive mean matching imputation with complex survey data. Survey Methodology, Catalogue No. 12-001-X, Vol. 47, No. 1. Paper available at http://www.statcan.gc.ca/pub/12-001-x/2021001/article/00009-eng.htm.
[53] CHEN, S. and HAZIZA, D. (2021). A review of multiply robust estimation with missing data. Invited submission of Springer Book Chapter.
[52] CHEN, S., HAZIZA, D and MASHREGHI, Z. (2021). Multiply robust bootstrap variance estimation in the presence of singly imputed data. Journal of Survey Statistics and Methodology, 9, 810-832.
[51] FAVRE MARTINOZ*, C., HAZIZA, D. & BEAUMONT, J.-F. (2021). Robust estimation for skewed populations: a general approach. Canadian Journal of Statistics, 49, 471-496.
[50] HAZIZA, D. & VALLÉE*, A.-A. (2020). Variance estimation in the presence of singly imputed data: A critical review. Japanese Journal of Statistics and Data Science, 3, 583-623. Invited submission for a special issue in survey sampling.
[49] ZHAO, P., HAZIZA, D. & WU, C. (2020). Survey weighted estimating equation inference with nuisance functional. Journal of Econometrics, 216, 516-536.
[48] HAZIZA, D. & SMITH, P.A. (2019). An interview with Chris Skinner. International Statistical Review 87, 451-470.
[47] CHEN, S. & HAZIZA, D. (2019). Multiply robust nonparametric multiple imputation for the treatment of missing data. Statistica Sinica 29, 2035-2053.
[46] CHEN, S., HAZIZA, D., MASHREGHI*, Z. & LÉGER, C. (2019). Pseudo-population bootstrap methods for imputed survey data. Biometrika 106, 369-384.
[45] CHEN, S. & HAZIZA, D. (2019). Recent Developments in Dealing with Item Nonresponse in Surveys: a Critical Review. International Statistical Review 87, S192-S218.
[44] LESAGE, E., HAZIZA, D. & D' HAULTFOEUILLE, X. (2019). A cautionary tale on instrument vector calibration for the treatment of unit nonresponse in surveys. Journal of the American Statistical Association 526, 906-915.
[43] CHAPUT, H., CHAUVET, G, HAZIZA, D., SOLARD, J. & SALEMBIER, L. (2018). Joint imputation procedures for categorical variables. Statistics and Applications 16, 123-144. Invited paper for a special issue in honor of the 80th birthday of Professor J.N.K. Rao
[42] CHEN, S. & HAZIZA, D. (2018). Jackknife empirical likelihood method for multiply robust estimation with missing data. Computational Statistics and Data Analysis 127, 258-268.
[41] CHEN, S. & HAZIZA, D. (2017). Multiply robust imputation procedures for zero-inflated distributions in surveys, Metron 75, 333-343. Invited paper for a special issue of Metron.
[40] CHEN, Q., ELLIOT, M.R., HAZIZA, D., YANG, Y., GOSH, M., LITTLE, R., SEDRANSK, J. & THOMPSON, M. (2017). Approaches to improving survey estimates. Statistical Science 32, 227-248.
[39] HAZIZA, D. & BEAUMONT, J.-F. (2017). Construction of weights in surveys: a review. Statistical Science 32, 206-226.
[38] CHEN, S. & HAZIZA, D. (2017). Multiply robust imputation procedures for the treatment of item nonresponse in surveys. Biometrika 102, 439-453.
[37] CHAUVET, G., HAZIZA, D. & LESAGE, E. (2017). Examining some aspects of balanced sampling in surveys. Statistica Sinica 27, 313-334.
[36] GOSH, M. & HAZIZA, D. (2017). Revisiting Basu's circus example: another look at the Horvitz-Thompson estimator. Calcutta Statistical Association Bulletin 68, 33-37.
[35] BEAUMONT, J.-F. & HAZIZA, D. (2016). A note on the concept of invariance in two-phase sampling designs. Survey Methodology 42, 319-323.
[34] FAVRE-MARTINOZ*, C., HAZIZA, D. and BEAUMONT, J.-F. (2016). Robust inference in two-phase sampling designs with application to unit nonresponse. Scandinavian Journal of Statistics 43, 1019-1034.
[33] BOISTARD, H., CHAUVET, G. & HAZIZA, D. (2016). Doubly robust inference for the distribution function in the presence of missing survey data. Scandinavian Journal of Statistics 43, 683-699.
[32] MASHREGHI*, Z., HAZIZA, D. & LÉGER, C. (2016). A Survey of Bootstrap Methods in Finite Population Sampling. Statistics Surveys 10, 1-52.
[31] HAZIZA, D. & LESAGE, E. (2016). A discussion of weighting procedures for unit nonresponse. Journal of Official Statistics 32, 129-145.
[30] BEAUMONT, J.-F., BELIVEAU*, A. & HAZIZA, D. (2015). Clarifying some aspects of variance estimation in two-phase sampling. Journal of Survey Statistics and Methodology 3, 524-542.
[29] FAVRE-MARTINOZ*, C., HAZIZA, D. & BEAUMONT, J.-F. (2015). A method for determining the cut-off points for winsorized estimators with application to domain estimation. Survey Methodology 41, 57-77.
[28] HAZIZA, D., NAMBEU*, C.-O. & CHAUVET, G. (2014). Doubly robust imputation procedures for finite population means in the presence of a large number of zeroes. Canadian Journal of Statistics 42, 650-669.
[27] BEAUMONT, J.-F., HAZIZA, D. & BOCCI, C. (2014). An adaptive data collection procedure for call prioritization. Journal of Official Statistics 30, 607-621.
[26] GELEIN*, B., HAZIZA, D. & CAUSEUR, D. (2014). Preserving relationships between variables with MIVQUE based imputation for missing survey data. Journal of Multivariate Analysis 131, 197–208.
[25] MASHREGHI*, Z., LÉGER, C. & HAZIZA, D. (2014). Bootstrap Methods for Imputed Data from Regression, Ratio and Hot Deck Imputation. Canadian Journal of Statistics 42, 142-167.
[24] KIM, J.K. & HAZIZA, D. (2014). Doubly robust inference with missing data in survey sampling. Statistica Sinica 24, 375-394.
[23] DONGMO JIONGO*, V., HAZIZA, D. & DUCHESNE, P. (2013). Controlling the bias of robust small area estimators. Biometrika 100, 843-858.
[22] BEAUMONT, J.-F., HAZIZA, D. & RUIZ-GAZEN, A. (2013). A unified approach to robust estimation in finite population sampling. Biometrika 100, 555-569.
[21] HAZIZA, D. & PICARD*, F. (2012). Doubly robust point and variance estimation in the presence of imputed survey data. Canadian Journal of Statistics 40, 259-281.
[20] YUNG, W. & HAZIZA, D. (2012). Comment on the paper "Bias-adjustment and calibration of jackknife variance estimator in the presence of non-response". Journal of Statistical Planning and Inference 142, 2232-2240.
[19] CHAUVET, G. & HAZIZA, D. (2012). Fully efficient estimation of coefficients of correlation in the presence of imputed survey data. Canadian Journal of Statistics 40, 124-149.
[18] HAZIZA, D, HIDIROGLOU, M. A & RAO, J.N.K. (2011). Comparison of variance estimators in two-phase sampling: an empirical investigation. Pakistan Journal of Statistics 27, 477-492. Invited submission for a special issue in honour of Ken Brewer.
[17] CHAUVET, G., DEVILLE J.C. & HAZIZA, D. (2011). On balanced random imputation in surveys. Biometrika 98, 459-471.
[16] BEAUMONT, J.-F., HAZIZA, D. & BOCCI, C. (2011). Variance estimation under auxiliary value imputation. Statistica Sinica 21, 515-538.
[15] HAZIZA, D. & RAO, J.N.K. (2010). Variance estimation in two-stage sampling under imputation for missing survey data. Journal of Statistical Theory and Practice 4, 827-848. Invited submission for H.C. Gupta memorial special issue.
[14] TILLÉ, Y. & HAZIZA, D. (2010). An interesting property of the entropy of some sampling designs. Survey Methodology 36, 229-231.
[13] HAZIZA, D., CHAUVET, G. & DEVILLE J.C. (2010). A note on sampling and estimation in the presence of cut-off sampling. Australian and New Zealand Journal of Statistics 52, 303-319.
[12] HAZIZA, D., THOMPSON, K.J. & YUNG, W (2010). The effect of nonresponse adjustments on variance estimation. Survey Methodology 36, 35-43.
[11] HAZIZA, D. (2009), Imputation and inference in the presence of missing data, Handbook of Statistics, Volume 29, Sample Surveys: Theory Methods and Inference, Editors: C.R. Rao and D. Pfeffermann, 215-246.
[10] HIDIROGLOU, M.A., RAO, J.N.K. & HAZIZA, D. (2009), Variance estimation in two phase sampling.Australian and New Zealand Journal of Statistics 51, 127-141.
[9] HAZIZA, D., MECATTI, F. & RAO, J.N.K. (2008), Approximate variance estimators under the Rao-Sampford design. Metron, 66, 91-108 Invited submission for a special issue in survey sampling.
[8] HAZIZA, D. (2007), Variance estimation for a ratio in the presence of imputed data. Survey Methodology 33, 159-166.
[7] HAZIZA, D. & KUROMI, G. (2007), Handling item nonresponse in surveys. Journal of Case Studies in Business, Industry and Government statistics 1, 102-118.
[6] HAZIZA, D. & BEAUMONT, J-F. (2007), On the construction of imputation classes in surveys. International Statistical Review 75, 25-43.
[5] HAZIZA, D. & RAO, J. N. K. (2006), A nonresponse model approach to inference under imputation for missing survey data, Survey Methodology 32, 53-64.
[4] HAZIZA, D. (2005), Inférence en présence d’imputation simple dans les enquêtes: un survol, Journal de la Société Française de Statistique 146, 69-118.
[3] HAZIZA, D. & RAO, J. N. K. (2005), Inference for domains under imputation for missing data, The Canadian Journal of Statistics 33, 149-161.
[2] ARAGON, Y., HAZIZA, D. & RUIZ-GAZEN, A. (2005), Les simulations dans l'enseignement des sondages avec le logiciel Genesis sous SAS et la bibliothèque Sondages sous R, Modulad 32, 86-91.
[1] HAZIZA, D. & RAO, J. N. K. (2003), Inference for population means under unweighted imputation for missing survey data, Survey Methodology 29, 81-90.
Articles in revision/submitted in peer-reviewed journals
[72] EUSTACHE*, E., DADOUG*, & HAZIZA, D. (2023). On high-dimensional variance estimation in survey sampling. Submitted.
[71] DADOUG*, M., GOGA, C. & HAZIZA, D. (2023). Statistical Inference in the presence of imputed survey data through random forests. Submitted.
[70] LARBI*, K., TSANG*, J., HAZIZA, D. & DAGDOUG*, M. (2023). On the use of machine learning procedures for the treatment of unit nonresponse in surveys. Submitted.
[69] BERTARELLI, G., SCHIRIPPA SPAGNOLO, F., SALVATI, N., HAZIZA, D. & CHAMBERS, R. (2022). Bias control for M-quantile-based small area estimators. In revision for the Journal of the American Statistical Association.
Conference proceedings
HAZIZA, D. (2013). Estimation robuste en présence de valeurs influentes dans les enquêtes. Proceedings of the Journées Statistiques de la Société Française De Statistique 2013.
BOISTARD, H., CHAUVET, G. & HAZIZA, D. (2012). Consistance sous un modèle de réponse de la fonctiond de répartition estimée en présence de données manquantes. Proceedings of the JMS 2012.
HAZIZA, D., DONGMO JIONGO, V.& DUCHESNE, P. (2012). Triple robustesse en présence de données imputées dans les enquêtes. Proceedings of the JMS 2012.
HAZIZA, D. & BEAUMONT, J-F. (2011).Robust inference in two-phase sampling designs with application to unit nonresponse. Proceedings of ITSEW 2011.
BEAUMONT, J-F., & HAZIZA, D. . (2011). A theoretical framework for responsive designs. Proceedings of ITSEW 2011.
NAMBEU, C.-O, HAZIZA, D. & CHAUVET, G. (2011). Imputation pour des populations contenant beaucoup de zéros. Proceedings of the Annual Conference of the Statistical Society of Canada.
KIM, J.K. & HAZIZA, D. (2010). Doubly robust inference with missing survey data. Proceedingsof the Survey Methods Section, American Statistical Association.
HAZIZA, D. (2010). Resampling methods for variance estimation in the presence of missing survey data. Proceedings of the Annual Meeting of the Italian Statistical Society.
BEAUMONT, J.-F., HAZIZA, D. & RUIZ-GAZEN, A. (2009). A unified approach to robust estimation in finite population sampling. Proceedings of the International Statistical Institute, Durban.
HAZIZA, D. & PICARD, F. (2008), Jackknife variance estimation in the presence of imputed data. Proceedings of the Workshop on Calibration and Estimation in Surveys.
HAZIZA, D., KUROMI, G. & BÉRUBÉ, J. (2007), Sampling and estimation in the presence of tax data in business surveys. Proceedings of the International Conference on Establishment Surveys II, CD-ROM.
HAZIZA, D. (2006), Estimation en présence de données fiscales dans les enquêtes économiques, Actes des Journées de Statistique de la Société Française de Statistique, CD-ROM.
HAZIZA, D. & RAO, J. N. K. (2005), Une approche par modèle de non-réponse pour l’inférence en présence de données imputées, Actes des Journées de Méthodologie Statistique 2005. Disponible sur la page http://jms.insee.fr/site/.
HAZIZA, D. & BEAUMONT, J-F. (2005), Estimation simplifiée de la variance dans le cas de l’échantillonnage à deux phases in Méthodes d’enquêtes et sondages, Lavallée, P. and Rivest, L.P., editors, 372-377. Dunod.
HAZIZA, D. & RAO, J. N. K. (2004). Inférence pour des statistiques bivariées en présence d’imputation dans le cas d’enquêtes stratifiées à degrés multiples, in Échantillonnage et méthodes d’enquêtes, Ardilly, P. Editor, 189-196. Dunod.
HAZIZA, D., MECATTI, F. & RAO, J. N. K. (2004), Comparison of variance estimators under Rao-Sampford method: a simulation study. Proceedings of the Survey Methods Section, American Statistical Association, CD-Rom.
HAZIZA, D. (2003), GENESIS, a methodological and pedagogical tool, Proceedings of the Survey Methods Section, American Statistical Association, CD-Rom.
BEAUMONT, J-F, HAZIZA, D., MITCHELL, C. & RANCOURT, E. (2003), New tools at Statistics Canada to measure and evaluate the impact of nonresponse and imputation, Proceedings of the 2003 FCSM conference.
HAZIZA, D. & RAO, J. N. K. (2001), Inference for regression coefficients under imputation for missing data, Proceedings of the Survey Methods Section, Statistical Society of Canada, 61-66.
HAZIZA, D. & RAO, J. N. K. (2001), Model-assisted approach to inference for totals in cluster sampling under imputation for missing data, Proceedings of the Survey Methods Section, American Statistical Association, CD-Rom.
HAZIZA, D., CHOW, O., CHARBONNIER, C. and BEAUMONT, J.F. (2001), Construction of Imputation Cells in the Canadian Labour Force Survey, Proceedings of Statistics Canada Symposium 2001, CD-Rom.
HAZIZA, D. and RAO, J. N. K. (2000), Inference for domain means under imputation for missing data, Proceedings of the Survey Methods Section, Statistical Society of Canada, 197-202.
Articles in the Imputation Bulletin
HAZIZA, D. (2007). Frameworks for variance estimation in the presence of imputed data, The Imputation Bulletin, vol 7, no 1.
HAZIZA, D. (2006). Simulation studies in the presence of nonresponse and imputation, The Imputation Bulletin, vol 6, no 1.
HAZIZA, D. & RANCOURT, E. (2004). Variance estimation under the two-phase imputation model approach, The Imputation Bulletin, vol 4, no 1.
HAZIZA, D. (2003). Proc MI and Proc MIANALYZE in SAS, The Imputation Bulletin, vol 3, no 2.
HAZIZA, D. (2002). Distortion of distributions, The Imputation Bulletin, vol 2, no 2.
HAZIZA, D. (2002). GENESIS, The Imputation Bulletin, vol 2, no 2.
HAZIZA, D. (2002). Imputation classes, The Imputation Bulletin, vol 2, no 1.
HAZIZA, D. (2001). The risks of imputation, The Imputation Bulletin, vol 1, no 2.
HAZIZA, D. (2001). Why do we impute?, The Imputation Bulletin, vol 1, no 1.
*Indicates a PhD or a M.Sc. student
[68] CHEN, S., HAZIZA, D. & MICHAL*, V. (2023). Efficient multiply robust imputation in the presence of influential units in survey. To appear in the Canadian Journal of Statistics.
[67] CHEN, S., & HAZIZA, D. (2023). Doubly and multiply robust imputation procedures in surveys. The Survey Statistician 88, 75-85. Invited submission.
[66] ARDILLY, P., HAZIZA, D., LAVALLÉE, P & TILLÉ, Y. (2023). On the contributions of Jean-Claude Deville to the theory of survey sampling and official statistics. To appear in Survey Methodology. Invited submission.
[65] CHEN, S. & HAZIZA, D. (2023). A unified framework of multiply robust estimation approaches for handling incomplete data. Computational Statistics and Data Analysis 179, pp. 1-17
[64] DAGDOUG*, M., GOGA, C. & HAZIZA, D. (2023). Model-assisted estimation through random forests in finite population sampling. Journal of the American Statistical Association 542, 1234-1251.
[63] DAGDOUG*, M., GOGA, C. & HAZIZA, D. (2023). Model-assisted estimation in high-dimensional settings for survey data. Journal of Applied Statistics 50, 761-785.
[62] CHEN, S. & HAZIZA, D. (2023). General purpose multiply robust data integration procedures for handling non-probability samples. Scandinavian Journal of Statistics 50, 697-724.
[61] DAGDOUG*, M., GOGA, C. & HAZIZA, D. (2023). Imputation procedures in surveys using nonparametric and machine learning methods: an empirical comparison. Journal of Survey Statistics and Methodology 11, 141-188.
[60] GOCHANOUR, B., CHEN, S., BEEBE, L. & HAZIZA, D. (2023). A nonparametric multiply robust multiple imputation method for causal inference. Metrika 86, 517-542.
[59] CHEN, S., HAZIZA, D and MASHREGHI, Z. (2022). A comparison of existing bootstrap algorithms for multi-stage sampling.
STATS 5, 521-537. nvited submission for a special issue on Bootstrap methods
[58] NEUSY, E., BEAUMONT, J.-F., YUNG, W., HIDIROGLOU, M., & HAZIZA, D. (2022). Nonresponse Follow-up for Business surveys. Survey Methodology 48, 95-117.
[57] BEAUMONT, J.-F. & HAZIZA, D. (2022). Statistical inference from finite population samples: a critical review of frequentist and
Bayesian approaches. Canadian Journal of Statistics 50, 1186-1212. Invited submission.
[56] ZHAO, P., HAZIZA, D. & WU, C. (2022). Empirical Likelihood Inference for Complex Surveys and the Design-based Oracle Variable Selection Theory. Statistica Sinica, 32, 435-457.
[55] HAZIZA, D., CHEN, S. & GAO*, Y. (2022). Targeting key survey variables at the nonresponse treatment stage. Journal of Survey Statistics and Methodology, 10, 25-49.
[54] CHEN, S., HAZIZA, D. & STUBBLEFIELD, A. (2021). A note on multiply robust predictive mean matching imputation with complex survey data. Survey Methodology, Catalogue No. 12-001-X, Vol. 47, No. 1. Paper available at http://www.statcan.gc.ca/pub/12-001-x/2021001/article/00009-eng.htm.
[53] CHEN, S. and HAZIZA, D. (2021). A review of multiply robust estimation with missing data. Invited submission of Springer Book Chapter.
[52] CHEN, S., HAZIZA, D and MASHREGHI, Z. (2021). Multiply robust bootstrap variance estimation in the presence of singly imputed data. Journal of Survey Statistics and Methodology, 9, 810-832.
[51] FAVRE MARTINOZ*, C., HAZIZA, D. & BEAUMONT, J.-F. (2021). Robust estimation for skewed populations: a general approach. Canadian Journal of Statistics, 49, 471-496.
[50] HAZIZA, D. & VALLÉE*, A.-A. (2020). Variance estimation in the presence of singly imputed data: A critical review. Japanese Journal of Statistics and Data Science, 3, 583-623. Invited submission for a special issue in survey sampling.
[49] ZHAO, P., HAZIZA, D. & WU, C. (2020). Survey weighted estimating equation inference with nuisance functional. Journal of Econometrics, 216, 516-536.
[48] HAZIZA, D. & SMITH, P.A. (2019). An interview with Chris Skinner. International Statistical Review 87, 451-470.
[47] CHEN, S. & HAZIZA, D. (2019). Multiply robust nonparametric multiple imputation for the treatment of missing data. Statistica Sinica 29, 2035-2053.
[46] CHEN, S., HAZIZA, D., MASHREGHI*, Z. & LÉGER, C. (2019). Pseudo-population bootstrap methods for imputed survey data. Biometrika 106, 369-384.
[45] CHEN, S. & HAZIZA, D. (2019). Recent Developments in Dealing with Item Nonresponse in Surveys: a Critical Review. International Statistical Review 87, S192-S218.
[44] LESAGE, E., HAZIZA, D. & D' HAULTFOEUILLE, X. (2019). A cautionary tale on instrument vector calibration for the treatment of unit nonresponse in surveys. Journal of the American Statistical Association 526, 906-915.
[43] CHAPUT, H., CHAUVET, G, HAZIZA, D., SOLARD, J. & SALEMBIER, L. (2018). Joint imputation procedures for categorical variables. Statistics and Applications 16, 123-144. Invited paper for a special issue in honor of the 80th birthday of Professor J.N.K. Rao
[42] CHEN, S. & HAZIZA, D. (2018). Jackknife empirical likelihood method for multiply robust estimation with missing data. Computational Statistics and Data Analysis 127, 258-268.
[41] CHEN, S. & HAZIZA, D. (2017). Multiply robust imputation procedures for zero-inflated distributions in surveys, Metron 75, 333-343. Invited paper for a special issue of Metron.
[40] CHEN, Q., ELLIOT, M.R., HAZIZA, D., YANG, Y., GOSH, M., LITTLE, R., SEDRANSK, J. & THOMPSON, M. (2017). Approaches to improving survey estimates. Statistical Science 32, 227-248.
[39] HAZIZA, D. & BEAUMONT, J.-F. (2017). Construction of weights in surveys: a review. Statistical Science 32, 206-226.
[38] CHEN, S. & HAZIZA, D. (2017). Multiply robust imputation procedures for the treatment of item nonresponse in surveys. Biometrika 102, 439-453.
[37] CHAUVET, G., HAZIZA, D. & LESAGE, E. (2017). Examining some aspects of balanced sampling in surveys. Statistica Sinica 27, 313-334.
[36] GOSH, M. & HAZIZA, D. (2017). Revisiting Basu's circus example: another look at the Horvitz-Thompson estimator. Calcutta Statistical Association Bulletin 68, 33-37.
[35] BEAUMONT, J.-F. & HAZIZA, D. (2016). A note on the concept of invariance in two-phase sampling designs. Survey Methodology 42, 319-323.
[34] FAVRE-MARTINOZ*, C., HAZIZA, D. and BEAUMONT, J.-F. (2016). Robust inference in two-phase sampling designs with application to unit nonresponse. Scandinavian Journal of Statistics 43, 1019-1034.
[33] BOISTARD, H., CHAUVET, G. & HAZIZA, D. (2016). Doubly robust inference for the distribution function in the presence of missing survey data. Scandinavian Journal of Statistics 43, 683-699.
[32] MASHREGHI*, Z., HAZIZA, D. & LÉGER, C. (2016). A Survey of Bootstrap Methods in Finite Population Sampling. Statistics Surveys 10, 1-52.
[31] HAZIZA, D. & LESAGE, E. (2016). A discussion of weighting procedures for unit nonresponse. Journal of Official Statistics 32, 129-145.
[30] BEAUMONT, J.-F., BELIVEAU*, A. & HAZIZA, D. (2015). Clarifying some aspects of variance estimation in two-phase sampling. Journal of Survey Statistics and Methodology 3, 524-542.
[29] FAVRE-MARTINOZ*, C., HAZIZA, D. & BEAUMONT, J.-F. (2015). A method for determining the cut-off points for winsorized estimators with application to domain estimation. Survey Methodology 41, 57-77.
[28] HAZIZA, D., NAMBEU*, C.-O. & CHAUVET, G. (2014). Doubly robust imputation procedures for finite population means in the presence of a large number of zeroes. Canadian Journal of Statistics 42, 650-669.
[27] BEAUMONT, J.-F., HAZIZA, D. & BOCCI, C. (2014). An adaptive data collection procedure for call prioritization. Journal of Official Statistics 30, 607-621.
[26] GELEIN*, B., HAZIZA, D. & CAUSEUR, D. (2014). Preserving relationships between variables with MIVQUE based imputation for missing survey data. Journal of Multivariate Analysis 131, 197–208.
[25] MASHREGHI*, Z., LÉGER, C. & HAZIZA, D. (2014). Bootstrap Methods for Imputed Data from Regression, Ratio and Hot Deck Imputation. Canadian Journal of Statistics 42, 142-167.
[24] KIM, J.K. & HAZIZA, D. (2014). Doubly robust inference with missing data in survey sampling. Statistica Sinica 24, 375-394.
[23] DONGMO JIONGO*, V., HAZIZA, D. & DUCHESNE, P. (2013). Controlling the bias of robust small area estimators. Biometrika 100, 843-858.
[22] BEAUMONT, J.-F., HAZIZA, D. & RUIZ-GAZEN, A. (2013). A unified approach to robust estimation in finite population sampling. Biometrika 100, 555-569.
[21] HAZIZA, D. & PICARD*, F. (2012). Doubly robust point and variance estimation in the presence of imputed survey data. Canadian Journal of Statistics 40, 259-281.
[20] YUNG, W. & HAZIZA, D. (2012). Comment on the paper "Bias-adjustment and calibration of jackknife variance estimator in the presence of non-response". Journal of Statistical Planning and Inference 142, 2232-2240.
[19] CHAUVET, G. & HAZIZA, D. (2012). Fully efficient estimation of coefficients of correlation in the presence of imputed survey data. Canadian Journal of Statistics 40, 124-149.
[18] HAZIZA, D, HIDIROGLOU, M. A & RAO, J.N.K. (2011). Comparison of variance estimators in two-phase sampling: an empirical investigation. Pakistan Journal of Statistics 27, 477-492. Invited submission for a special issue in honour of Ken Brewer.
[17] CHAUVET, G., DEVILLE J.C. & HAZIZA, D. (2011). On balanced random imputation in surveys. Biometrika 98, 459-471.
[16] BEAUMONT, J.-F., HAZIZA, D. & BOCCI, C. (2011). Variance estimation under auxiliary value imputation. Statistica Sinica 21, 515-538.
[15] HAZIZA, D. & RAO, J.N.K. (2010). Variance estimation in two-stage sampling under imputation for missing survey data. Journal of Statistical Theory and Practice 4, 827-848. Invited submission for H.C. Gupta memorial special issue.
[14] TILLÉ, Y. & HAZIZA, D. (2010). An interesting property of the entropy of some sampling designs. Survey Methodology 36, 229-231.
[13] HAZIZA, D., CHAUVET, G. & DEVILLE J.C. (2010). A note on sampling and estimation in the presence of cut-off sampling. Australian and New Zealand Journal of Statistics 52, 303-319.
[12] HAZIZA, D., THOMPSON, K.J. & YUNG, W (2010). The effect of nonresponse adjustments on variance estimation. Survey Methodology 36, 35-43.
[11] HAZIZA, D. (2009), Imputation and inference in the presence of missing data, Handbook of Statistics, Volume 29, Sample Surveys: Theory Methods and Inference, Editors: C.R. Rao and D. Pfeffermann, 215-246.
[10] HIDIROGLOU, M.A., RAO, J.N.K. & HAZIZA, D. (2009), Variance estimation in two phase sampling.Australian and New Zealand Journal of Statistics 51, 127-141.
[9] HAZIZA, D., MECATTI, F. & RAO, J.N.K. (2008), Approximate variance estimators under the Rao-Sampford design. Metron, 66, 91-108 Invited submission for a special issue in survey sampling.
[8] HAZIZA, D. (2007), Variance estimation for a ratio in the presence of imputed data. Survey Methodology 33, 159-166.
[7] HAZIZA, D. & KUROMI, G. (2007), Handling item nonresponse in surveys. Journal of Case Studies in Business, Industry and Government statistics 1, 102-118.
[6] HAZIZA, D. & BEAUMONT, J-F. (2007), On the construction of imputation classes in surveys. International Statistical Review 75, 25-43.
[5] HAZIZA, D. & RAO, J. N. K. (2006), A nonresponse model approach to inference under imputation for missing survey data, Survey Methodology 32, 53-64.
[4] HAZIZA, D. (2005), Inférence en présence d’imputation simple dans les enquêtes: un survol, Journal de la Société Française de Statistique 146, 69-118.
[3] HAZIZA, D. & RAO, J. N. K. (2005), Inference for domains under imputation for missing data, The Canadian Journal of Statistics 33, 149-161.
[2] ARAGON, Y., HAZIZA, D. & RUIZ-GAZEN, A. (2005), Les simulations dans l'enseignement des sondages avec le logiciel Genesis sous SAS et la bibliothèque Sondages sous R, Modulad 32, 86-91.
[1] HAZIZA, D. & RAO, J. N. K. (2003), Inference for population means under unweighted imputation for missing survey data, Survey Methodology 29, 81-90.
Articles in revision/submitted in peer-reviewed journals
[72] EUSTACHE*, E., DADOUG*, & HAZIZA, D. (2023). On high-dimensional variance estimation in survey sampling. Submitted.
[71] DADOUG*, M., GOGA, C. & HAZIZA, D. (2023). Statistical Inference in the presence of imputed survey data through random forests. Submitted.
[70] LARBI*, K., TSANG*, J., HAZIZA, D. & DAGDOUG*, M. (2023). On the use of machine learning procedures for the treatment of unit nonresponse in surveys. Submitted.
[69] BERTARELLI, G., SCHIRIPPA SPAGNOLO, F., SALVATI, N., HAZIZA, D. & CHAMBERS, R. (2022). Bias control for M-quantile-based small area estimators. In revision for the Journal of the American Statistical Association.
Conference proceedings
HAZIZA, D. (2013). Estimation robuste en présence de valeurs influentes dans les enquêtes. Proceedings of the Journées Statistiques de la Société Française De Statistique 2013.
BOISTARD, H., CHAUVET, G. & HAZIZA, D. (2012). Consistance sous un modèle de réponse de la fonctiond de répartition estimée en présence de données manquantes. Proceedings of the JMS 2012.
HAZIZA, D., DONGMO JIONGO, V.& DUCHESNE, P. (2012). Triple robustesse en présence de données imputées dans les enquêtes. Proceedings of the JMS 2012.
HAZIZA, D. & BEAUMONT, J-F. (2011).Robust inference in two-phase sampling designs with application to unit nonresponse. Proceedings of ITSEW 2011.
BEAUMONT, J-F., & HAZIZA, D. . (2011). A theoretical framework for responsive designs. Proceedings of ITSEW 2011.
NAMBEU, C.-O, HAZIZA, D. & CHAUVET, G. (2011). Imputation pour des populations contenant beaucoup de zéros. Proceedings of the Annual Conference of the Statistical Society of Canada.
KIM, J.K. & HAZIZA, D. (2010). Doubly robust inference with missing survey data. Proceedingsof the Survey Methods Section, American Statistical Association.
HAZIZA, D. (2010). Resampling methods for variance estimation in the presence of missing survey data. Proceedings of the Annual Meeting of the Italian Statistical Society.
BEAUMONT, J.-F., HAZIZA, D. & RUIZ-GAZEN, A. (2009). A unified approach to robust estimation in finite population sampling. Proceedings of the International Statistical Institute, Durban.
HAZIZA, D. & PICARD, F. (2008), Jackknife variance estimation in the presence of imputed data. Proceedings of the Workshop on Calibration and Estimation in Surveys.
HAZIZA, D., KUROMI, G. & BÉRUBÉ, J. (2007), Sampling and estimation in the presence of tax data in business surveys. Proceedings of the International Conference on Establishment Surveys II, CD-ROM.
HAZIZA, D. (2006), Estimation en présence de données fiscales dans les enquêtes économiques, Actes des Journées de Statistique de la Société Française de Statistique, CD-ROM.
HAZIZA, D. & RAO, J. N. K. (2005), Une approche par modèle de non-réponse pour l’inférence en présence de données imputées, Actes des Journées de Méthodologie Statistique 2005. Disponible sur la page http://jms.insee.fr/site/.
HAZIZA, D. & BEAUMONT, J-F. (2005), Estimation simplifiée de la variance dans le cas de l’échantillonnage à deux phases in Méthodes d’enquêtes et sondages, Lavallée, P. and Rivest, L.P., editors, 372-377. Dunod.
HAZIZA, D. & RAO, J. N. K. (2004). Inférence pour des statistiques bivariées en présence d’imputation dans le cas d’enquêtes stratifiées à degrés multiples, in Échantillonnage et méthodes d’enquêtes, Ardilly, P. Editor, 189-196. Dunod.
HAZIZA, D., MECATTI, F. & RAO, J. N. K. (2004), Comparison of variance estimators under Rao-Sampford method: a simulation study. Proceedings of the Survey Methods Section, American Statistical Association, CD-Rom.
HAZIZA, D. (2003), GENESIS, a methodological and pedagogical tool, Proceedings of the Survey Methods Section, American Statistical Association, CD-Rom.
BEAUMONT, J-F, HAZIZA, D., MITCHELL, C. & RANCOURT, E. (2003), New tools at Statistics Canada to measure and evaluate the impact of nonresponse and imputation, Proceedings of the 2003 FCSM conference.
HAZIZA, D. & RAO, J. N. K. (2001), Inference for regression coefficients under imputation for missing data, Proceedings of the Survey Methods Section, Statistical Society of Canada, 61-66.
HAZIZA, D. & RAO, J. N. K. (2001), Model-assisted approach to inference for totals in cluster sampling under imputation for missing data, Proceedings of the Survey Methods Section, American Statistical Association, CD-Rom.
HAZIZA, D., CHOW, O., CHARBONNIER, C. and BEAUMONT, J.F. (2001), Construction of Imputation Cells in the Canadian Labour Force Survey, Proceedings of Statistics Canada Symposium 2001, CD-Rom.
HAZIZA, D. and RAO, J. N. K. (2000), Inference for domain means under imputation for missing data, Proceedings of the Survey Methods Section, Statistical Society of Canada, 197-202.
Articles in the Imputation Bulletin
HAZIZA, D. (2007). Frameworks for variance estimation in the presence of imputed data, The Imputation Bulletin, vol 7, no 1.
HAZIZA, D. (2006). Simulation studies in the presence of nonresponse and imputation, The Imputation Bulletin, vol 6, no 1.
HAZIZA, D. & RANCOURT, E. (2004). Variance estimation under the two-phase imputation model approach, The Imputation Bulletin, vol 4, no 1.
HAZIZA, D. (2003). Proc MI and Proc MIANALYZE in SAS, The Imputation Bulletin, vol 3, no 2.
HAZIZA, D. (2002). Distortion of distributions, The Imputation Bulletin, vol 2, no 2.
HAZIZA, D. (2002). GENESIS, The Imputation Bulletin, vol 2, no 2.
HAZIZA, D. (2002). Imputation classes, The Imputation Bulletin, vol 2, no 1.
HAZIZA, D. (2001). The risks of imputation, The Imputation Bulletin, vol 1, no 2.
HAZIZA, D. (2001). Why do we impute?, The Imputation Bulletin, vol 1, no 1.
