A Two-Stage Approach to Missing Data: Theory and Application to Auxiliary Variables
By Victoria Savalei, Peter M. Bentler. 2009
96 citations recorded by the source corpus.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/a43d351d4f7f8da2a0cc49a0614ddbd6800e1eca
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Statistics and Probability
- Carl F. Falk
- Paul T. von Hippel
- Mijke Rhemtulla
- Richard P. Bagozzi
- Youjae Yi
- Victoria Savalei
- Craig K. Enders
- T. Macías
Citations in the corpus
- New Confidence Intervals and Bias Comparisons Show That Maximum Likelihood Can Beat Multiple Imputation in Small Samples (2013, methodology): “The analytic model need not be identical to the model that is used to maximize the likelihood, but it must be possible to derive the parameters of the analytic model from the parameters of the model that is used to maximize the likelihood (Savalei & Bentler, 2009; Yuan & Lu, 2008).”
- Specification, evaluation, and interpretation of structural equation models (2012, methodology): “Two improved procedures for handling missing data are case-wise maximum likelihood (Jamshidian and Bentler 1999) and an approach based on missing at random (Savalei and Bentler 2009).”
- Robust Two-Stage Approach Outperforms Robust Full Information Maximum Likelihood With Incomplete Nonnormal Data (2014, methodology): “Corrections to standard errors and test statistics to accompany TS estimates have been developed for both normal data (Cai, 2008; Cai & Lee, 2009; Savalei & Bentler, 2007, 2009) and nonnormal data (Yuan & Bentler, 2000; Yuan & Lu, 2008).”
- On Obtaining Estimates of the Fraction of Missing Information From Full Information Maximum Likelihood (2012, methodology): “Relative efficiencies are frequently used to compare estimators in simulation studies (eg, Savalei & Bentler, 2009 ).”
- Understanding Robust Corrections in Structural Equation Modeling (2014, methodology): “For example, when the data are incomplete and nonnormal, the so called two-stage estimation approach can be accompanied by robust corrections that adjust for incomplete and/or nonnormal data (Savalei & Bentler, 2009; Savalei & Falk, 2011; Yuan & Bentler, 2000).”