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• W3102867057 abstract "When analyzing incomplete data, is it better to use multiple imputation (MI) or full information maximum likelihood (ML)? In large samples ML is clearly better, but in small samples ML’s usefulness has been limited because ML commonly uses normal test statistics and confidence intervals that require large samples. We propose small-sample t-based ML confidence intervals that have good coverage and are shorter than t-based confidence intervals under MI. We also show that ML point estimates are less biased and more efficient than MI point estimates in small samples of bivariate normal data. With our new confidence intervals, ML should be preferred over MI, even in small samples, whenever both options are available." @default.
• W3102867057 created "2020-11-23" @default.
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• W3102867057 creator A5063926783 @default.
• W3102867057 date "2015-10-12" @default.
• W3102867057 modified "2023-10-18" @default.
• W3102867057 title "New Confidence Intervals and Bias Comparisons Show That Maximum Likelihood Can Beat Multiple Imputation in Small Samples" @default.
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• W3102867057 doi "https://doi.org/10.1080/10705511.2015.1047931" @default.
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