Daniel L. Oberski

Statistics and Probability · 2013–2021

A researcher in the Science corpus: 5 papers; trusts 31 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 29 citations into it; #3,335 of 12,932 by trust here, #8,279 by citations
• Artificial Intelligence — 16 citations into it
• Management Science and Operations Research — 11 citations into it; #8,230 of 11,946 by trust here, #11,360 by citations
• Computer Networks and Communications — 9 citations into it
• Environmental Engineering — 7 citations into it; #852 of 5,479 by trust here, #4,165 by citations
• Experimental and Cognitive Psychology — 7 citations into it
• Global and Planetary Change — 6 citations into it
• Sociology and Political Science — 5 citations into it

Whom Daniel L. Oberski trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. T. Asparouhov
2. Laura M. Stapleton
3. Thomas Lumley
4. Alexander Kukush
5. Kenneth A. Bollen
6. Rex Bryan Kline
7. Willem E. Saris
8. Drew A. Linzer
9. S. van Buuren
10. Zsuzsa Bakk

Who trusts Daniel L. Oberski overall

1. Teresa Limpo
2. Jeroen K. Vermunt
3. F. Seaton
4. T. Kashdan
5. Rui Alexandre Alves
6. M. Hickendorff
7. Jay Magidson
8. P. George
9. Melissa Stiksma
10. V. Connelly

Papers from Daniel L. Oberski's perspective

Ordered by how strongly the researchers Daniel L. Oberski trusts endorse each paper. Same corpus, a different order for every researcher.

1. Analysis of Incomplete Multivariate Data (Technometrics)
2. Book Review : Structural Equations with Latent Variables Kenneth A. Bollen New York: Wiley, 1989, 514 pp., approx. $44.95
3. Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators (Political Analysis)
4. Sampling Weights in Latent Variable Modeling
5. Latent Class Modeling with Covariates: Two Improved Three-Step Approaches (Political Analysis)
6. The Incorporation of Sample Weights Into Multilevel Structural Equation Models
7. Generalized Latent Variable Modeling: Multilevel,Longitudinal, and Structural Equation Models (Technometrics)
8. BEYOND SEM: GENERAL LATENT VARIABLE MODELING
9. Categorical Data Analysis
10. A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity

Their papers

• Measurement Error Models With Uncertainty About the Error Variance (2013) — 50 citations
• lavaan.survey: An R Package for Complex Survey Analysis of Structural Equation Models (2014) — 429 citations
• Bias-Adjusted Three-Step Latent Markov Modeling With Covariates (2016) — 55 citations
• Can’t see the wood for the trees? An assessment of street view- and satellite-derived greenness measures in relation to mental health (2021) — 101 citations
• Mixture Models: Latent Profile and Latent Class Analysis (2016) — 536 citations