Zsuzsa Bakk
Statistics and Probability · 2013–2016
A researcher in the Science corpus: 2 papers; trusts 14 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 13 citations into it; #7,335 of 12,932 by trust here, #7,165 by citations
- Artificial Intelligence — 7 citations into it; #16,493 of 35,345 by trust here, #22,463 by citations
- Genetics — 5 citations into it; #1,946 of 6,854 by trust here, #4,339 by citations
Whom Zsuzsa Bakk trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jeroen K. Vermunt
- B. Muthén
- H. Petras
- T. Asparouhov
- Jay Magidson
- Stephanie T. Lanza
- Marcel A. Croon
- Nele de Cuyper
- Katherine E. Masyn
- Alexandre J. S. Morin
Who trusts Zsuzsa Bakk overall
- Daniel L. Oberski
- Karen Nylund-Gibson
- Zachary K. Collier
- Katherine E. Masyn
- Jeroen K. Vermunt
- K. Schnitzler
- Daniel Spurk
- Walter L. Leite
- E. Gonida
- A. Hirschi
Papers from Zsuzsa Bakk's perspective
Ordered by how strongly the researchers Zsuzsa Bakk trusts endorse each paper. Same corpus, a different order for every researcher.
- Latent Variable Analysis: Growth Mixture Modeling and Related Techniques for Longitudinal Data
- Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators (Political Analysis)
- Latent Class Modeling with Covariates: Two Improved Three-Step Approaches (Political Analysis)
- Distributional assumptions of growth mixture models: implications for overextraction of latent trajectory classes. (Psychological methods)
- General Growth Mixture Analysis with Antecedents and Consequences of Change
- Latent Class Analysis With Distal Outcomes: A Flexible Model-Based Approach (Structural equation modeling : a multidisciplinary journal)
- A Multifoci Person-Centered Perspective on Workplace Affective Commitment: A Latent Profile/Factor Mixture Analysis
- Analysis of Incomplete Multivariate Data (Technometrics)
- Latent Class Analysis
- Building an identifiable latent class model with covariate effects on underlying and measured variables