Wu, C. and Thompson, M.E. (2020).
*Sampling Theory and Practice.* Springer, Cham. (17 chapters, 365 pages)

Wu, C. and Sitter, R.R. (2001). A Model-calibration Approach to Using Complete Auxiliary Information from Survey Data. *Journal of the American Statistical Association*, 96, 185–193.

Sitter, R.R. and Wu, C. (2002). Efficient Estimation of Quadratic Finite Population Functions in the Presence of Auxiliary Information.* Journal of the American Statistical Association*, 97, 535–543.

Chen, J. and Wu, C. (2002). Estimation of Distribution Function and Quantiles Using the Model-calibrated Pseudo Empirical Likelihood Method. *Statistica Sinica*, 12, 1223–1239.

Wu, C. (2003). Optimal Calibration Estimators in Survey Sampling. *Biometrika*, 90, 935–951.

Wu, C. and Luan*, Y. (2003). Optimal Calibration Estimators Under Two-phase Sampling. *Journal of Official Statistics*, 19, 119–131.

Zhang*, S., Han, P. and Wu, C. (2023). Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference. *International Statistical Review*, 91, 165–192.

Wu, C. (2023). Calibration Techniques for Model-based Prediction and Doubly Robust Estimation. *The Survey Statistician*, 88, 86–93.

Wu, C. (2004). Combining Information From Multiple Surveys Through Empirical Likelihood Method. *The Canadian Journal of Statistics*, 32, 15–26.

Wu, C. (2004). Weighted Empirical Likelihood Inference. *Statistics and Probability Letters*, 66, 67–79.

Wu, C. and Rao, J.N.K. (2006). Pseudo Empirical Likelihood Ratio Confidence Intervals for Complex Surveys. *The Canadian Journal of Statistics*, 34, 359–375.

Fu, Y., Wang, X. and Wu, C. (2009). Weighted Empirical Likelihood Inference for Multiple Samples. *Journal of Statistical Planning and Inference*, 139, 1462–1473.

Rao, J.N.K. and Wu, C. (2009). Empirical Likelihood Methods. In *Handbook of Statistics, Volume 29B, Sample Surveys: Inference and Analysis*, editors: D. Pfeffermann and C.R. Rao, 189–207.

Rao, J.N.K. and Wu, C. (2010). Pseudo Empirical Likelihood Inference for Multiple Frame Surveys. *Journal of the American Statistical Association*, 105, 1494–1503.

Rao, J.N.K. and Wu, C. (2010). Bayesian Pseudo Empirical Likelihood Intervals for Complex Surveys. *Journal of the Royal Statistical Society, Series B*, 72, 533–544.

Zhao*, P. and Wu, C. (2019). Some Theoretical and Practical Issues with Empirical Likelihood Methods for Complex Surveys. *International Statistical Review*, 87, S239–256.

Zhao*, P., Ghosh, M., Rao, J.N.K. and Wu, C. (2020). Bayesian Empirical Likelihood Inference with Complex Survey Data. *Journal of the Royal Statistical Society, Series B*, 82, 155–174.

Zhao*, P., Rao, J.N.K. and Wu, C. (2020). Empirical Likelihood Inference with Public-Use Survey Data. *Electronic Journal of Statistics*, 14, 2484–2509.

Wu, C. and Thompson, M.E. (2020). Empirical Likelihood and Estimating Equations for Survey Data Analysis. *Japanese Journal of Statistics and Data Science*, 3, 565–581.

Zhao*, P., Haziza, D. and Wu, C. (2022). Sample Empirical Likelihood and the Design-based Oracle Variable Selection Theory. *Statistica Sinica*, 32, 435–457.

Chen, J., Sitter, R.R. and Wu, C. (2002). Using Empirical Likelihood Method to Obtain Range Restricted Weights in Regression Estimators for Surveys. *Biometrika*, 89, 230–237.

Wu, C. (2004). Some Algorithmic Aspects of the Empirical Likelihood Method in Survey Sampling. *Statistica Sinica*, 14, 1057–1067.

Wu, C. (2005). Algorithms and R Codes for the Pseudo Empirical Likelihood Method in Survey Sampling. *Survey Methodology*, 31, 239–243.

Wu, C. and Lu, W.W. (2016). Calibration Weighting Methods for Complex Surveys. *International Statistical Review*, 84, 79–98.

Chen, J., Thompson, M.E. and Wu, C. (2004). Estimation of Fish Abundance Indices Based on Scientific Research Trawl Surveys. *Biometrics*, 60, 116–123.

Wu, C., Thompson, M.E., Fong, G.T., Jiang, Y., Yan, Y., Feng, G. and Li, Q. (2010). Methods of the International Tobacco Control (ITC) China Survey. *Tobacco Control*, 19 (Suppl 2), i1–i5.

Wu, C., Thompson, M.E., Fong, G.T., Jiang, Y., Yang, Y., Feng, G. and Quah, A.C.K. (2015). Methods of the International Tobacco Control (ITC) China Survey: Waves 1, 2 and 3. *Tobacco Control*, 24 (Suppl 4), iv1–iv5. DOI: 10.1136/tobaccocontrol-2014-052025

Raina, P. et al. (2019). Cohort Profile: The Canadian Longitudinal Study on Aging (CLSA). *International Journal of Epidemiology*, 48, 1752–1753j. doi: 10.1093/ije/dyz173

Carrillo*, I.A., Chen, J. and Wu, C. (2010). The Pseudo-GEE Approach to the Analysis of Longitudinal Surveys. *The Canadian Journal of Statistics*, 38, 540–554.

Carrillo*, I.A., Chen, J. and Wu, C. (2011). Pseudo-GEE Approach to Analyzing Longitudinal Surveys under Imputation for Missing Responses. *Journal of Official Statistics*, 27, 255–277.

Chen*, Z., Yi, G.Y. and Wu, C. (2011). Marginal Methods for Correlated Binary Data with Misclassified Responses. *Biometrika*, 98, 647–662.

Chen*, Z., Yi, G. Y. and Wu, C. (2014). Marginal Analysis of Longitudinal Ordinal Data with Misclassification in Both Response and Covariates. *Biometrical Journal*, 56(1), 69–85.

Zhao*, J., Cook, R.J. and Wu, C. (2015). Multiple Imputation for the Analysis of Incomplete Compound Variables. *The Canadian Journal of Statistics*, 43, 240–264.

Chen*, M., Wu, C. and Thompson, M.E. (2015). An Imputation-based Empirical Likelihood Approach to Pretest-Posttest Studies. *The Canadian Journal of Statistics*, 43, 378–402.

Chen*, M., Wu, C. and Thompson, M.E. (2016). Mann-Whitney Test with Empirical Likelihood Methods for Pretest-Posttest Studies. *Journal of Nonparametric Statistics*, 28, 360–374.

Chen*, M., Thompson, M.E. and Wu, C. (2018). Empirical Likelihood Methods for Complex Surveys with Data Missing-by-Design. *Statistica Sinica*, 28, 2027–2048.

Chen*, Z., Mantel, H., Wu, C. and Yi, Y.Y. (2018). Regression Analysis of Binary Data from Complex Surveys with Misclassification in Ordinal Covariates. *Statistics and Applications*, 16, 105–122.

Zhang*, S., Han, P. and Wu, C. (2019). A Unified Empirical Likelihood Approach to Testing MCAR and Subsequent Estimation. *Scandinavian Journal of Statistics*, 46, 272–288.

Zhang*, S., Han, P. and Wu, C. (2019). Empirical Likelihood Inference for Non- randomized Pretest-Posttest Studies with Missing Data. *Electronic Journal of Statistics*, 13, 2012–2042.

She*, X. and Wu, C. (2019). Fully Efficient Joint Fractional Imputation for Incomplete Bivariate Ordinal Responses. *Statistica Sinica*, 29, 409–430.

She*, X. and Wu, C. (2020). Validity and Efficiency in Analyzing Ordinal Responses with Missing Observations. *The Canadian Journal of Statistics*, 48, 138–151.

Zhang*, S., Han, P. and Wu, C. (2020). A Multiply Robust Mann-Whitney Test for Non-randomized Pretest-Posttest Studies with Missing Data. *Journal of Nonparametric Statistics*, 32, 323–344.

So*, H.Y., Thompson, M.E. and Wu, C. (2020). Correlated and Misclassified Binary Observations in Complex Surveys. *The Canadian Journal of Statistics*, 48, 633–654.

Wu, C. and Xu, B. (2008). Deflator Selection and Generalized Linear Modelling in Market-based Regression Analysis. *Applied Financial Economics*, 18, 1739–1753.

Qin, Y.S., Rao, J.N.K. and Wu, C. (2010). Empirical Likelihood Confidence Intervals for the Gini Measure of Income Inequality. *Economic Modelling*, 27, 1429–1435.

Zhao*, P., Haziza, D. and Wu, C. (2020). Survey Weighted Estimating Equations Inferences With Nuisance Functionals. *Journal of Econometrics*, 216, 516–536.

Yuan*, M., Li, P. and Wu, C. (2023). Semiparametric Inference on Gini Indices of Two Semicontinuous Populations Under Density Ratio Models. *The Econometrics Journal*, 26, 174–188.

Zhao, P. and Wu, C. (2024). Augmented Two-Step Estimating Equations with Nuisance Functionals and Complex Surveys. *The Econometrics Journal*, 27, 37–61.

Wu, C. and Sitter, R.R. (2001). Variance Estimation for the Finite Population Distribution Function with Complete Auxiliary Information. *The Canadian Journal of Statistics*, 29, 289–307.

Thompson, M.E. and Wu, C. (2008). Simulation-based Randomized Systematic PPS Sampling Under
Substitution of Units. *Survey Methodology*, 34, 3–10.

Wu, C. and Rao, J.N.K. (2010). Bootstrap Procedures for the Pseudo Empirical Likelihood Method in Sample Surveys. *Statistics and Probability Letters*, 80, 1472–1478.

Wu, C. and Yan*, Y. (2012). Empirical Likelihood Inference for Two-Sample Problems. *Statistics and Its Interface*, 5, 345–354.

Kim, J.K. and Wu, C. (2013). Sparse and Efficient Replication Variance Estimation for Complex Surveys. *Survey Methodology*, 39, 91–120.

Tan, Z. and Wu, C. (2015). Generalized Pseudo Empirical Likelihood Inferences for Complex Surveys. *The Canadian Journal of Statistics*, 43, 1–17.

Chen*, Y., Li, P. and Wu, C. (2020). Doubly Robust Inference with Non-probability Survey Samples. *Journal of the American Statistical Association*, 115, 2011–2021.

Kim, J.K., Park, S., Chen*, Y. and Wu, C. (2021). Combining Non-probability and Probability Survey Samples Through Mass Imputation. *Journal of the Royal Statistical Society, Series A*, 184, 941–963.

Chen*, Y., Li, P., Rao, J.N.K. and Wu, C. (2022). Pseudo Empirical Likelihood Methods for Non-probability Survey Samples. *The Canadian Journal of Statistics*, 50, 1166–1185.

Wu, C. (2022). Statistical Inference with Non-probability Survey Samples (with Discussion). *Survey Methodology*, 48, 283–311.

Chen*, Y., Li, P. and Wu, C. (2023). Dealing with Undercoverage for Non-probability Survey Samples. *Survey Methodology*, 49, 497–515.

Sitter, R.R. and Wu, C. (2001). A Note on Woodruff Confidence Intervals for Quantiles. *Statistics and Probability Letters*, 52, 353–358.

Tsao, M. and Wu, C. (2006). Empirical Likelihood Inference for a Common Mean in the Presence of Heteroscedasticity. *The Canadian Journal of Statistics*, 34, 45–59.

Yuan*, M., Li, P. and Wu, C. (2021). Semiparametric Inference of the Youden Index and the Optimal Cut-off Point under Density Ratio Models. *The Canadian Journal of Statistics*, 49, 965–986.

Yuan*, M., Li, P. and Wu, C. (2022). Semiparametric Empirical Likelihood Inference with General Estimating Equations under Density Ratio Models. *Electronic Journal of Statistics*, 16, 5321–5377.