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Comparison of confidence intervals for correlation coefficients based on incomplete monotone samples and those based on listwise deletion

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  • Krishnamoorthy, K.

Abstract

Inferential procedures for estimating and comparing normal correlation coefficients based on incomplete samples with a monotone missing pattern are considered. The procedures are based on the generalized variable (GV) approach. It is shown that the GV methods based on complete or incomplete samples are exact for estimating or testing a simple correlation coefficient. Procedures based on incomplete samples for comparing two overlapping dependent correlation coefficients are also proposed. For both problems, Monte Carlo simulation studies indicate that the inference based on incomplete samples and those based on samples after listwise or pairwise deletion are similar, and the loss of efficiency by ignoring additional data is not appreciable. The proposed GV approach is simple, and it can be readily extended to other problems such as the one of estimating two non-overlapping dependent correlations. The results are illustrated using two examples.

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  • Krishnamoorthy, K., 2013. "Comparison of confidence intervals for correlation coefficients based on incomplete monotone samples and those based on listwise deletion," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 378-388.
    • Handle: RePEc:eee:jmvana:v:114:y:2013:i:c:p:378-388
    DOI: 10.1016/j.jmva.2012.08.003
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    References listed on IDEAS

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    1. Hao, Jian & Krishnamoorthy, K., 2001. "Inferences on a Normal Covariance Matrix and Generalized Variance with Monotone Missing Data," Journal of Multivariate Analysis, Elsevier, vol. 78(1), pages 62-82, July.
    2. Chang, Wan-Ying & Richards, Donald St. P., 2010. "Finite-sample inference with monotone incomplete multivariate normal data, II," Journal of Multivariate Analysis, Elsevier, vol. 101(3), pages 603-620, March.
    3. Chang, Wan-Ying & Richards, Donald St.P., 2009. "Finite-sample inference with monotone incomplete multivariate normal data, I," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 1883-1899, October.
    4. Yu, Jianqi & Krishnamoorthy, K. & Pannala, Maruthy K., 2006. "Two-sample inference for normal mean vectors based on monotone missing data," Journal of Multivariate Analysis, Elsevier, vol. 97(10), pages 2162-2176, November.
    5. K. Krishnamoorthy & Maruthy Pannala, 1998. "Some Simple Test Procedures for Normal Mean Vector with Incomplete Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 50(3), pages 531-542, September.
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