David Haziza
Département de mathématiques et de statistique

Peer-reviewed publications

 

*Indicates a PhD or a master’s student

 

[41] CHEN, S. & HAZIZA, D. (2017). Multiply robust imputation procedures for zero-inflated distributions in surveys. 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, 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] BEAUMONT, J.-F. & HAZIZA, D. (2016). A note on the concept of invariance in two-phase sampling designs. Survey Methodology,42, 319-323

[35] FAVRE-MARTINOZ*, C., HAZIZA, D. and BEAUMONT, J.-F.  (2016). Robust inference in two-phase sampling designs with application to unit nonresponseScandinavian Journal of Statistics, 43, 1019-1034.

[34] GOSH, M. & HAZIZA, D. (2016). Revisiting Basu's circus example: another look at the Horvitz-Thompson estimatorCalcutta Statistical Association Bulletin. Invited paper.

[33] BOISTARD, H., CHAUVET, G. & HAZIZA, D.  (2016). Doubly robust inference for the distribution function in the presence of missing survey dataScandinavian Journal of Statistics, 43, 683-699.

[32] MASHREGHI*, Z., HAZIZA, D. & LÉGER, C.  (2016). A Survey of Bootstrap Methods in Finite Population SamplingStatistics 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 dataJournal 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

 

[48] LESAGE, E.,  HAZIZA, D. & D' Hautlfoeuille, X.  (2016). A cautionary tale on instrument vector calibration for the treatment of unit nonresponse in surveys. In revision for Journal of the American Statistical Association.

[47] MASHREGHI*, Z., HAZIZA, D. & LÉGER, C. (2015). Pseudo-population bootstrap methods for imputed survey data. In revision for Biometrika.

[46] CHAPUT, H., CHAUVET, G, HAZIZA, D., SOLARD, J. & SALEMBIER, L. (2015). Joint imputation procedures for categorical variables. In revision for Survey Methodology.

[45] HAZIZA, D. & VALLÉE*, A.-A.  (2015). Variance estimation in the presence of imputed data for high entropy sampling designsIn revision for Survey Methodology.

[44] ZHAO, P., HAZIZA, D. & WU, C. (2016). Empirical Likelihood Inference for Complex Surveys and the Design-based Oracle Variable Selection Theory. Submitted.

[43] CHEN, S. & HAZIZA, D. (2017). Multiply robust nonparametric multiple imputation for the treatment of missing data. In revision for Statistica Sinica.

[42] CHEN, S. & HAZIZA, D. (2016). Jackknife empirical likelihood method for multiply robust estimation with missing data. Submitted.

 

 

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 zérosProceedings 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.