Victoria Savalei
Statistics and Probability · 2005–2019
A researcher in the Science corpus: 13 papers; trusts 83 researchers and is trusted by 45 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 108 citations into it; #887 of 12,932 by trust here, #974 by citations
- Management Science and Operations Research — 71 citations into it; #819 of 11,946 by trust here, #1,155 by citations
- Computer Networks and Communications — 49 citations into it
- Artificial Intelligence — 28 citations into it; #26,120 of 35,345 by trust here, #32,362 by citations
- Food Science — 16 citations into it; #315 of 1,700 by trust here, #1,632 by citations
- Experimental and Cognitive Psychology — 13 citations into it; #1,371 of 9,731 by trust here, #7,398 by citations
- Clinical Psychology — 9 citations into it
- Developmental and Educational Psychology — 6 citations into it
Whom Victoria Savalei trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Craig K. Enders
- Peter M. Bentler
- J. Graham
- Roderick J. A. Little
- Lyle Ungar
- Donald B. Rubin
- Ke-Hai Yuan
- Rachel T. Fouladi
- Joseph L. Schafer
- J. Nevitt
Who trusts Victoria Savalei overall
- Dexin Shi
- Carl F. Falk
- C. Distefano
- Paul T. von Hippel
- Albert Maydeu-Olivares
- J. Veale
- Jin Liu
- Yan Xia
- Mike W.-L. Cheung
- R. van de Schoot
Papers from Victoria Savalei's perspective
Ordered by how strongly the researchers Victoria Savalei trusts endorse each paper. Same corpus, a different order for every researcher.
- Analysis of Incomplete Multivariate Data (Technometrics)
- Statistical Analysis With Missing Data
- INFERENCE AND MISSING DATA
- Statistical Analysis with Missing Data: Little/Statistical Analysis with Missing Data
- Missing data: our view of the state of the art. (Psychological methods)
- Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
- Missing data
- The Relative Performance of Full Information Maximum Likelihood Estimation for Missing Data in Structural Equation Models
- Estimating the Dimension of a Model
- Adding Missing-Data-Relevant Variables to FIML-Based Structural Equation Models
Their papers
- Assessing Mediational Models: Testing and Interval Estimation for Indirect Effects (2010) — 367 citations
- When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. (2012) — 2,457 citations
- Understanding Robust Corrections in Structural Equation Modeling (2014) — 156 citations
- Small Sample Statistics for Incomplete Nonnormal Data: Extensions of Complete Data Formulae and a Monte Carlo Comparison (2010) — 77 citations
- A Statistically Justified Pairwise ML Method for Incomplete Nonnormal Data: A Comparison With Direct ML and Pairwise ADF (2005) — 92 citations
- The Relationship Between Root Mean Square Error of Approximation and Model Misspecification in Confirmatory Factor Analysis Models (2012) — 105 citations
- A Two-Stage Approach to Missing Data: Theory and Application to Auxiliary Variables (2009) — 96 citations
- On Obtaining Estimates of the Fraction of Missing Information From Full Information Maximum Likelihood (2012) — 72 citations
- Is the ML Chi-Square Ever Robust to Nonnormality? A Cautionary Note With Missing Data (2008) — 71 citations
- Don’t Forget the Model in Your Model-based Reliability Coefficients: A Reply to McNeish (2018) (2019) — 77 citations
- What to Do About Zero Frequency Cells When Estimating Polychoric Correlations (2011) — 50 citations
- Bootstrapping Confidence Intervals for Fit Indexes in Structural Equation Modeling (2016) — 50 citations
- Robust Two-Stage Approach Outperforms Robust Full Information Maximum Likelihood With Incomplete Nonnormal Data (2014) — 50 citations