References
- Abd Majid, M. S., and S. H. Kassim. 2009. “Impact of the 2007 US Financial Crisis on the Emerging Equity Markets.” International Journal of Emerging Markets 4 (4): 341–357. doi:https://doi.org/10.1108/17468800910991241.
- Aerts, M., G. Claeskens, N. Hens, and G. Molenberghs. 2002. “Local Multiple Imputation.” Biometrika 89 (2): 375–388. doi:https://doi.org/10.1093/biomet/89.2.375.
- Allison, P. D. 2001. Missing Data. Thousand Oaks, California: Sage Publications.
- Balakrishnan, S., M. J. Wainwright, and B. Yu. 2017. “Statistical Guarantees for the EM Algorithm: From Population to Sample-based Analysis.” The Annals of Statistics 45 (1): 77–120. doi:https://doi.org/10.1214/16-AOS1435.
- Barnard, J., and D. B. Rubin. 1999. “Small-sample Degrees of Freedom with Multiple Imputation.” Biometrika 86 (4): 948–955. doi:https://doi.org/10.1093/biomet/86.4.948.
- Dempster, A. P., N. M. Laird, and D. B. Rubin. 1977. “Maximum Likelihood from Incomplete Data via the EM Algorithm.” Journal of the Royal Statistical Society: Series B (Methodological) 39 (1): 1–22.
- Higham, N. J. 2002. “Computing the Nearest Correlation Matrix—a Problem from Finance.” IMA Journal of Numerical Analysis 22 (3): 329–343. doi:https://doi.org/10.1093/imanum/22.3.329.
- Hüttner, A., M. Scherer, and B. Gräler. 2020. “Geostatistical Modeling of Dependent Credit Spreads: Estimation of Large Covariance Matrices and Imputation of Missing Data.” Journal of Banking Finance 118: 1–13. doi:https://doi.org/10.1016/j.jbankfin.2020.105897.
- Jennrich, R. I. 1962. “Missing Data Correlation Computations.” Mathematics of Computation 16 (80): 496–497. doi:https://doi.org/10.1090/S0025-5718-62-99194-9.
- Little, R. J. 1992. “Regression with Missing X’s: A Review.” Journal of the American Statistical Association 87 (420): 1227–1237.
- Nagy, K. 2020. “Term Structure Estimation with Missing Data: Application for Emerging Markets.” The Quarterly Review of Economics and Finance 75: 347–360. doi:https://doi.org/10.1016/j.qref.2019.04.002.
- Pigott, T. D. 2001. “A Review of Methods for Missing Data.” Educational Research and Evaluation 7 (4): 353–383. doi:https://doi.org/10.1076/edre.7.4.353.8937.
- Reiter, J. P. 2007. “Small-sample Degrees of Freedom for Multi-component Significance Tests with Multiple Imputation for Missing Data.” Biometrika 94 (2): 502–508. doi:https://doi.org/10.1093/biomet/asm028.
- Robins, J. M., and N. Wang. 2000. “Inference for Imputation Estimators.” Biometrika 87 (1): 113–124. doi:https://doi.org/10.1093/biomet/87.1.113.
- Rubin, D. B. 1976. “Inference and Missing Data.” Biometrika 63 (3): 581–592. doi:https://doi.org/10.1093/biomet/63.3.581.
- Rubin, D. B. 1987. Multiple Imputation for Nonresponse in Surveys. New York: John Wiley & Sons.
- Rubin, D. B. 1996. “Multiple Imputation after 18+ Years.” Journal of the American Statistical Association 91 (434): 473–489. doi:https://doi.org/10.1080/01621459.1996.10476908.
- Wang, N., and J. M. Robins. 1998. “Large-sample Theory for Parametric Multiple Imputation Procedures.” Biometrika 85 (4): 935–948. doi:https://doi.org/10.1093/biomet/85.4.935.
- Zhang, X. 2013. “Model Averaging with Covariates that are Missing Completely at Random.” Economics Letters 121 (3): 360–363. doi:https://doi.org/10.1016/j.econlet.2013.09.008.