Enders, CK. Applied Missing Data Analysis. New York: The Gulford Press; 2010.
Rubin, DB. Inference and missing data. Biometrika 1976, 63:581–592.
Little, RJA, Rubin, DB. Statistical Analysis with Missing Data. New York: Wiley; 2002.
Dixon, WJ. BMDP User`s Manual. Los Angeles, CA: BMDP Statistical Software; 1988.
Little, RJA. A test of missing completely at random for multivariate data with missing values. J Am Stat Assoc 1988, 83:1198–1202.
Park, T, Davis, CS. A test of the missing data mechanism for repeated categorical data. Biometrics 1993, 49:631–638.
Krishnamoorthy, K, Pannala, MK. Some simple test procedures for normal mean vector with incomplete data. Ann Inst Stat Mat 1998, 50:531–542.
Chen, HY, Little, R. A test of missing completely at random for generalized estimating equations with missing data. Biometrika 1999, 86:1–13.
Kim, KH, Bentler, PM. Tests of homogeneity of means and covariance matrices for multivariate incomplete data. Psychometrika 2002, 67:609–624.
Bentler, PM. EQS 6 Structural Equations Program Manual. Encino, CA: Multivariate Software, Inc; 2006.
Jamshidian, M, Jalal, S. Tests of homoscedasticity, normality, and missing completely at random for incomplete multivariate data. Psychometrika 2010, 75:649–674.
Hawkins, DM. A new test for multivariate normality and homoscedasticity. Technometrics 1981, 23:105–110.
Jamshidian, M, Jalal, S, Jansen, C. Missmech: A r package for testing homoscedasticity, multivariate normality, and missing completely at random (MCAR). Journal of Statistical Software, accepted.
Thoemmes, F, Enders, C. A structural equation model for testing whether data are missing completely at random. Paper Presented at the Annual Meeting of the American Educational Research Association, Chicago, 2007.
Jamshidian, M, Mata, M. Post modeling sensitivity analysis to detect the effect of missing data mechanisms. Multivariate Behav Res 2008, 43:432–452.
Jamshidian, M, Yuan, K‐H. Data‐driven sensitivity analysis to detect missing data mechanism with applications to structural equation modeling. J Stat Comput Simul 2013, 83:1344–1362.
Mardia, KV. Measures of multivariate skewness and kurtosis with applications. Biometrika 1970, 57:519–530.
Liang, J, Li, R, Fang, HB, Fang, KT. Testing multinormality based on low‐dimensional projection. J Stat Plan Infer 2000, 86:129–141.
Tan, M, Fang, H‐B, Tian, G‐L, Wei, G. Testing multivariate normality in incomplete data of small sample size. J Multivariate Anal 2005, 93:164–179.
Yuan, K‐H, Lambert, PL, Fouladi, RT. Mardia`s multivariate kurtosis with missing data. Multivariate Behav Res 2004, 39:413–437.
Savalei, V. A new measure of nonnormality based on the extension of mardia`s multivariate kurtosis for data that are missing at random. Under review, 2013.
Micceri, T. The unicorn, the normal curve, and other improbable creatures. Psychol Bull 1989, 105:156–166.
Scholz, FW, Stephens, MA. K‐sample Anderson‐Darling tests. Journal of the American Statistical Association 1987, 82:918–924.
Anderson, TW, Darling, DA. A test of goodness of fit. J Am Stat Assoc 1954, 49:765–769.
Westfall, PH, Young, S. Resampling‐Based Multiple Testing: Examples and Methods for p‐Value Adjustment. New York: Wiley; 1993.
Holland, B. Comment on false discovery rate‐adjusted multiple confidence intervals for selected parameter. J Am Stat Assoc 2005, 100:89–90.
Benjamini, Y, Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc [Ser B] 1995, 57:289–300.
Diggle, P, Kenward, MG. Informative drop‐out in longitudinal data analysis. Appl Stat 1994, 43:49–93.
Allison, P. Missing Data. Thousand Oaks, CA: Sage; 2001.
Bandeen‐roche, K, Miglioretti, DL, Zeger, SL, Rathouz, PJ. Latent variable regression for multiple discrete outcomes. J Am Stat Assoc 1997, 92:1375–1386.
Jung, H, Schafer, JL, Seo, B. A latent class selection model for nonignorably missing data. Comput Stat Data Anal 2011, 55:802–812.
Yuan, K‐H. Identifying variables responsible for data not missing at random. Psychometrika 2009a, 74:233–256.
Yuan, K‐H, Bentler, PM. Consistency of normal distribution based pseudo maximum likelihood estimates when data are missing at random. Am Stat 2010, 64:263–267.
Amemiya, T. Qualittive response models: a survey. J Econ Lit 1981, 19:1483–1536.
Molenberghs, G, Beunckens, C, Sotto, C, Kenward, MG. Every missingness not at random model has a missingness at random counterpart with equal fit. J R Stat Soc [Ser B] 2008, 70:371–388.
Yuan, K‐H. Normal distribution based pseudo ml for missing data: With applications to mean and covariance structure analysis. J Multivariate Anal 2009b, 100:1900–1918.
Yuan, K‐H, Yang‐Wallentin, F, Bentler, PM. Ml versus mi for missing data with violation of distribution conditions. Sociol Methods Res 2012, 41:598–629.
Kano, Y, Takai, K. Analysis of NMAR missing data without specifying missing‐data mechanisms in a linear latent variate model. J Multivariate Anal 2011, 102:1241–1255.