What is he best known for?
The fields of study he is best known for:
- Statistics
- Psychiatry
- Machine learning
His primary areas of investigation include Econometrics, Item response theory, Structural equation modeling, Statistics and Multilevel model.
His work deals with themes such as Cognitive psychology, Linear model, Spurious relationship, Statistical model and Mixture model, which intersect with Econometrics.
His Item response theory study combines topics in areas such as Regression analysis, Relation, Categorical variable and Applied mathematics.
His Regression analysis research focuses on Interaction and how it relates to Linear regression, Data mining, Range, Mathematical model and Computation.
His work on Moderated mediation and Sampling as part of general Statistics study is frequently connected to Context and Variance, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Multilevel model research is multidisciplinary, incorporating elements of Research design and Sampling distribution.
His most cited work include:
- Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis. (3505 citations)
- Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: new procedures and recommendations. (1044 citations)
- Probing Interactions in Fixed and Multilevel Regression: Inferential and Graphical Techniques (817 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Econometrics, Statistics, Mixture model, Multilevel model and Developmental psychology.
His work carried out in the field of Econometrics brings together such families of science as Structural equation modeling, Latent class model, Latent variable model, Item response theory and Statistical model.
Daniel J. Bauer interconnects Regression analysis, Interaction and Categorical variable in the investigation of issues within Item response theory.
His Regression analysis study combines topics from a wide range of disciplines, such as Statistical hypothesis testing and Linear regression.
His study ties his expertise on Random effects model together with the subject of Statistics.
His research integrates issues of Data mining, Linear model, Mathematical model, Mixed model and Data science in his study of Multilevel model.
He most often published in these fields:
- Econometrics (25.00%)
- Statistics (23.21%)
- Mixture model (15.18%)
What were the highlights of his more recent work (between 2018-2021)?
- Inference (7.14%)
- Algorithm (7.14%)
- Artificial intelligence (9.82%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Inference, Algorithm, Artificial intelligence, Noise and Sample size determination.
His Artificial intelligence research incorporates elements of Differential item functioning and Machine learning.
His study in the field of Covariate is also linked to topics like Process.
His Covariate study results in a more complete grasp of Econometrics.
His research in Sample size determination intersects with topics in Sampling frame, Descriptive statistics, Item response theory and Predictive validity.
The Item response theory study combines topics in areas such as Estimator, Autoencoder, Likelihood-ratio test and Importance sampling.
Between 2018 and 2021, his most popular works were:
- Simplifying the implementation of modern scale scoring methods with an automated R package: Automated moderated nonlinear factor analysis (aMNLFA). (11 citations)
- Simplifying the Assessment of Measurement Invariance over Multiple Background Variables: Using Regularized Moderated Nonlinear Factor Analysis to Detect Differential Item Functioning. (7 citations)
- Deep, spatially coherent Inverse Sensor Models with Uncertainty Incorporation using the evidential Framework (6 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Psychiatry
- Machine learning
Daniel J. Bauer mostly deals with Machine learning, Artificial intelligence, Differential item functioning, Inference and Covariate.
In most of his Machine learning studies, his work intersects topics such as Measurement invariance.
His Measurement invariance study combines topics from a wide range of disciplines, such as Latent variable, Type I and type II errors, Item response theory, Lasso and Likelihood-ratio test.
His Inference study incorporates themes from Contrast and State.
His work carried out in the field of Covariate brings together such families of science as Variation, Psychometrics and Set.
His Psychometrics research includes elements of Regularization and Categorical variable.
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